diff --git a/.gitignore b/.gitignore index b6e4761..65392f8 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,6 @@ +# Ignore data directory +data/ + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/Collect Tweets with Twint.ipynb b/Collect Tweets with Twint.ipynb new file mode 100644 index 0000000..35ff140 --- /dev/null +++ b/Collect Tweets with Twint.ipynb @@ -0,0 +1,327 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last updated: March 31, 2023\n", + "Last run: April, 2021\n", + "\n", + "**Data Collection**\n", + "\n", + "## Second-order Effects in Altmetrics: A Case Study Analyzing the Audiences of COVID-19 Research in the News and on Social Media\n", + "\n", + "Juan Pablo Alperin, Alice Fleerackers, Michelle Riedlinger & Stefanie Haustein\n", + "\n", + "**Related Publication:**\n", + "Alperin, J.P., Fleerackers, A., Riedlinger, M. & Haustein, S. (2023). Second-order Effects in Altmetrics: A Case Study Analyzing the Audiences of COVID-19 Research in the News and on Social Media. *Zenodo*. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Caveat: This code was cleaned up from its messy version that required solving many small data collection glitches in the original version. In particular, the original collection had some issues with twitter id's and twitter user id's being recorded in scientific notation. As such, it may not work perfectly.*\n", + "\n", + "*The code does faithfully captures the main approach and code used for data collection.*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import pandas as pd\n", + "import requests\n", + "\n", + "from tqdm.auto import tqdm\n", + "tqdm.pandas()\n", + "\n", + "from urllib.parse import unquote\n", + "\n", + "from pymed import PubMed\n", + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a file with the possible URLs for each article\n", + "Begins by finding a DOI for each Pubmed ID, then resolves (unshortens) the DOI URL." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Input a list of all the Pubmed IDs from our query\n", + "articles = pd.read_csv('data/covid_pubmed_ids-20210223.csv', header=None)\n", + "articles.columns = ['pmid']\n", + "\n", + "def get_doi(pmid):\n", + " randint = random.randint(0,10000)\n", + " email = 'nospam+%s@alperin.ca' % randint\n", + " pubmed = PubMed(tool=\"research\", email=email) \n", + " results = list(pubmed.query(pmid, max_results=1))\n", + " try: \n", + " article = results[0]\n", + " return article.doi\n", + " except: \n", + " return None\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fetches the DOI for each using the Pubmed API\n", + "articles['doi'] = articles.pmid.progress_apply(get_doi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def unshort(url):\n", + " try:\n", + " r = requests.get(url, allow_redirects=True, timeout=15)\n", + " return r.url\n", + " except:\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Take the DOI URL and resolve it to find out what it links to\n", + "articles['resolved_url'] = articles.doi.progress_apply(lambda doi: unshort('https://doi.org/%s' % doi))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "articles['doi_url1'] = articles.doi.map(lambda doi: 'https://doi.org/%s' % doi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Output file. This file was used as input for Crowdtangle Queries\n", + "articles.to_csv('data/covid_dois_in_4_outlets_with_urls.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "outlets_of_interest = ['MSN', 'New York Times', 'BBC News', 'The Guardian', 'Washington Post']\n", + "domains_of_interest = ['www.msn.com', 'www.nytimes.com', 'www.bbc.com', 'www.theguardian.com', 'www.washingtonpost.com']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect Twitter activity using Twint\n", + "All tweets collected are placed in a \"tweets\" folder. \n", + "(these cannot be made publicly available and would need to be collected again)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import twint\n", + "import nest_asyncio\n", + "nest_asyncio.apply() # makes things go faster by doing async searches\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure\n", + "def twint_search(url, outfile = False):\n", + " try:\n", + " c = twint.Config()\n", + " # Search for Everything in 2020 PLUS INCLUDE January 2021\n", + " c.Search = \"%s since:2020-01-01 until:2021-02-01 filter:links\" % unquote(url)\n", + " print(unquote(url))\n", + " c.Pandas = True\n", + " c.Hide_output = True\n", + "\n", + " # Run\n", + " twint.run.Search(c)\n", + " \n", + " search_results = twint.storage.panda.Tweets_df\n", + "\n", + " print(search_results.shape)\n", + "\n", + " if outfile and search_results.shape[0] > 0: \n", + " try: \n", + " tweets = pd.read_csv(outfile, dtype={'tweet_id': str, 'user_id_str': str}, low_memory=False)\n", + " tweets = tweets.append(search_results, ignore_index=True)\n", + " \n", + " except:\n", + " tweets = search_results\n", + " \n", + " tweets.to_csv(outfile, index=False)\n", + " \n", + " return search_results.shape[0]\n", + " except KeyboardInterrupt:\n", + " raise\n", + " except:\n", + " print(\"Error: %s\" % url)\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect Tweets about News Stories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# input file from Altmetric Explorer query\n", + "df = pd.read_csv('data/altmetric_news_mentions.csv')\n", + "\n", + "df['url_clean'] = df.URL.map(lambda x: x[:x.find('?')].strip('/') if x.find('?') > 0 else x.strip('/'))\n", + "df['domain'] = df.URL.map(lambda x: x.split('/')[2])\n", + "df = df[df.domain.isin(domains_of_interest)]\n", + "\n", + "story_urls = df[['outlet', 'URL', 'url_clean']].drop_duplicates(subset='url_clean')\n", + "\n", + "# story_urls.to_excel('data/altmetric_unique_story_urls_top5outlets.xlsx', index=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "# Collect all the tweets, one outlet at a time just to keep things tidier\n", + "\n", + "for outlet in outlets_of_interest:\n", + " tweets = None\n", + " now = datetime.datetime.now().strftime('%Y%m%d_%H%M')\n", + " outfile = 'tweets/%s_tweets_%s.csv' % (outlet.lower().replace(' ', '_'), now)\n", + "\n", + " print('Going after %s' % outlet)\n", + " story_urls.loc[story_urls.outlet == outlet, 'num_tweets'] = story_urls[story_urls.outlet == outlet].url_clean.progress_apply(lambda url: twint_search(url, outfile))\n", + " \n", + "## The final file used in research is a merger of all of the output files from this step\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect Tweets about research " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "all_urls = set(articles.resolved_url).union(articles.doi_url1)\n", + "\n", + "df = articles.drop_duplicates(subset='doi_url1')\n", + "tweets = None\n", + "\n", + "outfile = 'tweets/research_tweets.csv'\n", + "\n", + "# this collects the tweets and saves the num found for each one\n", + "# tweets themselves are saved to the outfile\n", + "df['num_tweets'] = df.doi_url1.progress_apply(lambda url: twint_search(url, outfile))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets = pd.read_csv('tweets/research_tweets.csv')\n", + "tweets.dropna(subset=['search'], inplace=True)\n", + "tweets['url_clean'] = tweets.search.map(lambda x: x.split(' ')[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tweets = tweets[tweets.search.notna()]\n", + "\n", + "tweets['url_clean'] = tweets.search.map(lambda x: x.split(' ')[0])\n", + "tweets.id = tweets.id.astype(str)\n", + "tweets = tweets[tweets['id'].notna()]\n", + "tweets.set_index('id', inplace=True)\n", + "tweets.index.name = 'tweet_id'\n", + "tweets.to_csv('citations/twint_research_url_mentions.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Compare Tweets of Research and News.ipynb b/Compare Tweets of Research and News.ipynb new file mode 100644 index 0000000..b4e8bb9 --- /dev/null +++ b/Compare Tweets of Research and News.ipynb @@ -0,0 +1,7718 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last updated: March 31, 2023\n", + "\n", + "**Main Analysis**\n", + "\n", + "## Second-order Effects in Altmetrics: A Case Study Analyzing the Audiences of COVID-19 Research in the News and on Social Media\n", + "\n", + "Juan Pablo Alperin, Alice Fleerackers, Michelle Riedlinger & Stefanie Haustein\n", + "\n", + "**Related Publication:**\n", + "Alperin, J.P., Fleerackers, A., Riedlinger, M. & Haustein, S. (2023). Second-order Effects in Altmetrics: A Case Study Analyzing the Audiences of COVID-19 Research in the News and on Social Media. *Zenodo*. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import datetime\n", + "import pandas as pd\n", + "import requests\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from matplotlib_venn import venn2 \n", + "from matplotlib_venn import venn3 \n", + "\n", + "import sqlite3 as lite\n", + "import json\n", + "from datetime import datetime\n", + "import time\n", + "import re\n", + "\n", + "plt.rcParams['figure.figsize'] = (10, 10)\n", + "\n", + "from IPython.core.display import Markdown as md\n", + "from IPython.core.display import HTML\n", + "pd.options.display.float_format = '{:30,.2f}'.format\n", + "\n", + "\n", + "def make_clickable(val):\n", + " # target _blank to open new window\n", + " return '{}'.format(val, val)\n", + "\n", + "def make_doi_clickable(val):\n", + " # target _blank to open new window\n", + " return '{}'.format(val, val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis of 2nd order effects for COVID research" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#helper function to have Unique Tweets and FB posts depending on level of aggregation\n", + "\n", + "def single_count(df, by_col = None):\n", + " id_col = set(df.columns).intersection(['tweet_id', 'platformId']).pop()\n", + " \n", + " if by_col: \n", + " return df.groupby([by_col, id_col]).last().reset_index()\n", + " else:\n", + " return df.drop_duplicates(subset=id_col)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "outlets_of_interest = ['MSN', 'New York Times', 'BBC News', 'The Guardian', 'Washington Post']\n", + "domains_of_interest = ['www.msn.com', 'www.nytimes.com', 'www.bbc.com', 'www.theguardian.com', 'www.washingtonpost.com']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "all_articles = pd.read_csv('data/covid_pubmed_ids-20210223.csv', header=None)\n", + "all_articles.columns = ['pmid']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# we deduplicate to have unique articles\n", + "articles = pd.read_csv('data/dois_in_4_outlets_with_urls.csv')[['doi', 'resolved_url']].drop_duplicates()\n", + "\n", + "def journal_from_url(url):\n", + " if 'bmj' in url:\n", + " return 'British Medical Journal'\n", + " elif 'medrxiv' in url: \n", + " return 'medRxiv'\n", + " elif 'biorxiv' in url:\n", + " return 'bioRxiv'\n", + " elif 'wiley' in url: \n", + " return 'Journal of Medical Virology'\n", + " \n", + "\n", + "articles['journal'] = articles.resolved_url.map(journal_from_url)\n", + "articles['is_preprint'] = articles.journal.map(lambda x: 'Rxiv' in x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# News Mentions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "news = pd.read_csv('data/altmetric_news_mentions.csv')\n", + "news['mention_date'] = pd.to_datetime(news.mention_date).dt.normalize()\n", + "\n", + "news.rename(columns={'url_clean': 'news_url'}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "1,406 mentions across 1,221 unique news stories" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('{:,} mentions across {:,} unique news stories'.format(news.shape[0], news.news_url.nunique())))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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num storiesnum mentions
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The Guardian111123
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" + ], + "text/plain": [ + " num stories num mentions\n", + "outlet \n", + "BBC News 78 83\n", + "MSN 743 820\n", + "New York Times 232 311\n", + "The Guardian 111 123\n", + "Washington Post 57 69" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = news.groupby('outlet').agg({'news_url': ['nunique', 'size']}).reset_index()\n", + "df.columns = ['outlet', 'num stories', 'num mentions']\n", + "df.set_index('outlet')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "344 (8.7%) of the 3,934 articles have at least one mention" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_news_pmids = len(news.pubmed_id.unique())\n", + "display(md('{} ({:,.1f}%) of the {:,} articles have at least one mention'.format(num_news_pmids,num_news_pmids/all_articles.shape[0]*100,all_articles.shape[0])))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This search revealed that 344 (8.7%) of the research articles in our sample were mentioned 1,406 times across 1,221 unique news stories. \n" + ] + } + ], + "source": [ + "print(\"This search revealed that {:,} ({:.1f}%) of the research articles in our sample were mentioned {:,} times across {:,} unique news stories. \".format(\n", + "num_news_pmids,num_news_pmids/all_articles.shape[0]*100, news.shape[0], news.news_url.nunique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On average, each article was written about 4.1 times (st. dev. = 6.5).\n" + ] + } + ], + "source": [ + "# Summary of Number of times each DOI gets written about on average\n", + "# By how many outlets\n", + "d = news.groupby('doi').size().describe()\n", + "\n", + "print(\"On average, each article was written about {:.1f} times (st. dev. = {:.1f}).\".format(d['mean'], d['std']))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(font_scale=1.5)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "ax = sns.histplot(news.groupby('doi').size(), kde=True)\n", + "plt.title('Distribution of Number of News Mentions by DOI')\n", + "\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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BBC NewsMSNNew York TimesThe GuardianWashington PostN
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British Medical Journal47262337322147
Journal of Medical Virology1185434739
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" + ], + "text/plain": [ + " BBC News MSN New York Times The Guardian \\\n", + "journal \n", + "British Medical Journal 47 262 33 73 \n", + "Journal of Medical Virology 11 85 43 4 \n", + "bioRxiv 5 116 92 10 \n", + "medRxiv 20 357 143 36 \n", + "\n", + " Washington Post N \n", + "journal \n", + "British Medical Journal 22 147 \n", + "Journal of Medical Virology 7 39 \n", + "bioRxiv 10 43 \n", + "medRxiv 30 115 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "narticles_per_journal = articles.groupby('journal').size()\n", + "narticles_per_journal.name = 'N'\n", + "df = news.groupby(['journal', 'outlet']).size().unstack()\n", + "df = df.join(narticles_per_journal)\n", + "df.to_clipboard()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Twitter Mentions" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "news_tweets = pd.read_csv('data/citations/twint_news_url_mentions.csv', dtype={'tweet_id': str, 'user_id_str': str}, na_values={'tweet_id': None}, low_memory=False)\n", + "\n", + "# some of the user_id_str's have a .0 at the end beacuse they were treated as a float at some \n", + "# point earlier in the pipeline\n", + "news_tweets['user_id_str'] = news_tweets.user_id_str.map(lambda x: str(x)[:-2] if str(x)[-2:] == '.0' else str(x))\n", + "\n", + "# get rid of these \n", + "news_tweets = news_tweets[news_tweets.tweet_id.notnull()]\n", + "\n", + "# rename to avoid ambiguity\n", + "news_tweets.rename(columns={'url_clean': 'news_url'}, inplace=True)\n", + "\n", + "news_tweets['created_at'] = pd.to_datetime(news_tweets.created_at)\n", + "news_tweets = news_tweets[news_tweets.created_at < '2021-02-01']\n", + "\n", + "\n", + "# There are a few duplicates caused by multiple moreover URLs resolving to the same thing\n", + "news_tweets = news_tweets.drop_duplicates(subset=['doi', 'tweet_id'])\n", + "\n", + "del news_tweets['journal']\n", + "news_tweets = news_tweets.merge(articles, how='left', left_on='doi', right_on='doi')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "97,235 Tweets linking to 486 (39.8%) of the 1,221 news stories." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "display(md(\"{:,} Tweets linking to {:,} ({:,.1f}%) of the 1,221 news stories.\".format(single_count(news_tweets).shape[0],\n", + " len(news_tweets.news_url.unique()),\n", + " len(news_tweets.news_url.unique())*100/news.news_url.nunique())))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['BBC News', 'MSN', 'New York Times', 'The Guardian', 'Washington Post',\n", + " 'N'],\n", + " dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "##\n", + "## Twint data for research mentions\n", + "##\n", + "\n", + "df = pd.read_csv('data/citations/twint_research_url_mentions.csv', dtype={'tweet_id': str, 'user_id_str': str}, na_values={'tweet_id': None}, low_memory=False)\n", + "\n", + "df['created_at'] = pd.to_datetime(df.created_at)\n", + "df = df[df.created_at < '2021-02-01']\n", + "\n", + "# get rid of these for now\n", + "df = df[df.tweet_id.notnull()]\n", + "\n", + "# This is different from the \"articles\" data because it has multiple URLs for every DOI\n", + "doi_to_url = pd.read_csv('data/dois_in_4_outlets_with_urls.csv')\n", + "df1 = doi_to_url[['doi', 'resolved_url']]\n", + "df1.columns = ['doi', 'url']\n", + "df2 = doi_to_url[['doi', 'doi_url1']]\n", + "df2.columns = ['doi', 'url']\n", + "doi_to_url = df1.append(df2).drop_duplicates().set_index('url')\n", + "\n", + "df = df.merge(doi_to_url, how='left', left_on='url_clean', right_index=True) \n", + "\n", + "# now that we're done, remove to avoid ambiguity\n", + "del df['url_clean']\n", + " \n", + "research_tweets = df.merge(articles, how='left', left_on='doi', right_on='doi')\n", + "del df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "50,299 Tweets linking to 325 (94.5%) of the 344 research articles." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md(\"{:,} Tweets linking to {} ({:,.1f}%) of the 344 research articles.\".format(single_count(research_tweets).shape[0], \n", + " len(research_tweets.doi.unique()),\n", + " len(research_tweets.doi.unique())*100/num_news_pmids)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FB Mentions" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "fb_reaction_cols = ['actualLikeCount', 'actualLoveCount', 'actualWowCount', 'actualHahaCount','actualSadCount', 'actualAngryCount', 'actualThankfulCount']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "news_fb = pd.read_csv('data/citations/crowdtangle_news_url_mentions.csv')\n", + "news_fb = news_fb[news_fb.platformId.map(lambda x: type(x) == str)] # loose a couple of malformed \n", + "#keep every entry only once per post/link combo | shouldn't be duplicates here anyway\n", + "news_fb = news_fb.drop_duplicates(subset=['platformId', 'originalLink'])\n", + "\n", + "news_fb.rename(columns={'date': 'created_at'}, inplace=True)\n", + "news_fb['created_at'] = pd.to_datetime(news_fb.created_at).dt.normalize()\n", + "\n", + "# Keep only things that go until December 31, 2020\n", + "news_fb = news_fb[news_fb.created_at < '2021-02-01']\n", + "\n", + "\n", + "news_fb['any_reaction'] = news_fb[fb_reaction_cols].sum(axis=1)\n", + "\n", + "news_fb = news_fb.merge(news[['news_url', 'doi', 'outlet']], how='left', left_on='originalLink', right_on='news_url')\n", + "news_fb = news_fb.merge(articles, how='left', left_on='doi', right_on='doi')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14,083 FB Posts linking to 516 (42.3%) of the 1,221 news stories.\n" + ] + } + ], + "source": [ + "print(\"{:,} FB Posts linking to {:,} ({:,.1f}%) of the 1,221 news stories.\".format(single_count(news_fb).shape[0],\n", + " len(news_fb.news_url.unique()),\n", + " len(news_fb.news_url.unique())*100/news.news_url.nunique()))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of Research FB Posts: 6,420\n" + ] + } + ], + "source": [ + "# based on resolved URL\n", + "research_fb = pd.read_csv('data/citations/crowdtangle_research_url_mentions.csv')\n", + "\n", + "#keep every entry only once per post/link combo \n", + "research_fb = research_fb.drop_duplicates(subset=['platformId', 'originalLink'])\n", + "\n", + "research_fb.rename(columns={'date': 'created_at'}, inplace=True)\n", + "research_fb['created_at'] = pd.to_datetime(research_fb.created_at).dt.normalize()\n", + "\n", + "# Keep only things that go until December 31, 2020\n", + "research_fb = research_fb[research_fb.created_at < '2021-02-01']\n", + "\n", + "\n", + "research_fb['page_id'] = research_fb.platformId.map(lambda x: str(x).split('_')[0])\n", + "research_fb['facebook_id'] = research_fb.platformId.map(lambda x: str(x).split('_')[1] if '_' in x else None)\n", + "\n", + "research_fb['any_reaction'] = research_fb[fb_reaction_cols].sum(axis=1)\n", + "\n", + "# This is different from the \"article\" data because it has multiple URLs for every DOI\n", + "df = pd.read_csv('data/dois_in_4_outlets_with_urls.csv')\n", + "df1 = df[['doi', 'resolved_url']]\n", + "df1.columns = ['doi', 'url']\n", + "df2 = df[['doi', 'doi_url1']]\n", + "df2.columns = ['doi', 'url']\n", + "doi_to_url = df1.append(df2).drop_duplicates().set_index('url')\n", + "\n", + "# add the DOI column\n", + "research_fb = research_fb.merge(doi_to_url, how='left', left_on='originalLink', right_on='url')\n", + "\n", + "# now that we're done, remove to avoid ambiguity\n", + "del research_fb['originalLink']\n", + "\n", + "# remove duplicates if we found the same post using two different URLs\n", + "research_fb = research_fb.drop_duplicates(subset=['platformId', 'doi'])\n", + "\n", + "# add the article info columns\n", + "research_fb = research_fb.merge(articles, how='left', left_on='doi', right_on='doi')\n", + "\n", + "print(\"Number of Research FB Posts: {:,}\".format(single_count(research_fb).shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6,420 FB Posts linking to 246 (71.5%) of the 344 research articles.\n" + ] + } + ], + "source": [ + "print(\"{:,} FB Posts linking to {} ({:,.1f}%) of the 344 research articles.\".format(single_count(research_fb).shape[0], \n", + " len(research_fb.doi.unique()),\n", + " len(research_fb.doi.unique())*100/num_news_pmids))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Start of analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Comparing research and news on Twitter" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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newsresearchmultiplier
num_posts_tw97,235.0050,299.001.93
num_spaces_tw60,296.0027,771.002.17
num_shares_tw412,509.00227,041.001.82
num_likes_tw1,111,458.00512,308.002.17
num_replies_tw89,509.0039,788.002.25
num_spaces_tw_avg0.620.551.12
num_shares_tw_avg4.244.510.94
num_likes_tw_avg11.4310.191.12
num_replies_tw_avg0.920.791.16
\n", + "
" + ], + "text/plain": [ + " news \\\n", + "num_posts_tw 97,235.00 \n", + "num_spaces_tw 60,296.00 \n", + "num_shares_tw 412,509.00 \n", + "num_likes_tw 1,111,458.00 \n", + "num_replies_tw 89,509.00 \n", + "num_spaces_tw_avg 0.62 \n", + "num_shares_tw_avg 4.24 \n", + "num_likes_tw_avg 11.43 \n", + "num_replies_tw_avg 0.92 \n", + "\n", + " research \\\n", + "num_posts_tw 50,299.00 \n", + "num_spaces_tw 27,771.00 \n", + "num_shares_tw 227,041.00 \n", + "num_likes_tw 512,308.00 \n", + "num_replies_tw 39,788.00 \n", + "num_spaces_tw_avg 0.55 \n", + "num_shares_tw_avg 4.51 \n", + "num_likes_tw_avg 10.19 \n", + "num_replies_tw_avg 0.79 \n", + "\n", + " multiplier \n", + "num_posts_tw 1.93 \n", + "num_spaces_tw 2.17 \n", + "num_shares_tw 1.82 \n", + "num_likes_tw 2.17 \n", + "num_replies_tw 2.25 \n", + "num_spaces_tw_avg 1.12 \n", + "num_shares_tw_avg 0.94 \n", + "num_likes_tw_avg 1.12 \n", + "num_replies_tw_avg 1.16 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count each tweet only once\n", + "\n", + "def summary_cols(platform):\n", + " return ['_'.join([c,platform]) for c in ['num_posts', 'num_spaces', 'num_shares', 'num_likes', 'num_replies']]\n", + "\n", + "def summarize_posts(df, platform, normalize=False):\n", + " names = summary_cols(platform)\n", + " agg_funcs = ['nunique', 'nunique', 'sum', 'sum', 'sum']\n", + " \n", + " if platform == 'tw':\n", + " cols = ['tweet_id', 'user_id_str', 'nretweets', 'nlikes', 'nreplies']\n", + " elif platform == 'fb':\n", + " cols = ['platformId', 'accountId', 'actualShareCount', 'any_reaction', 'actualCommentCount']\n", + "\n", + " agg_dict = {c: a for c, a in zip(cols, agg_funcs)}\n", + "\n", + " tmp = df[cols].agg(agg_dict)\n", + " \n", + " if type(tmp) == pd.Series:\n", + " tmp.index = names\n", + " tmp = tmp.astype(int)\n", + " \n", + " if normalize:\n", + " tmp2 = tmp[names[1:]].divide(tmp[names[0]])\n", + " tmp2.index = [x+'_avg' for x in names[1:]]\n", + " tmp = tmp.append(tmp2)\n", + " else:\n", + " tmp.columns = names\n", + " tmp = tmp.astype(int)\n", + " \n", + " if normalize:\n", + " tmp2= tmp[names[1:]].divide(tmp[names[0]], axis=0)\n", + " tmp2.columns = [x+'_avg' for x in names[1:]]\n", + " tmp = tmp.merge(tmp2, how='left', left_index=True, right_index=True)\n", + " \n", + " return tmp\n", + "\n", + "sum1 = summarize_posts(single_count(news_tweets), 'tw', True)\n", + "sum1.name = 'news'\n", + "\n", + "\n", + "sum2 = summarize_posts(single_count(research_tweets), 'tw', True)\n", + "sum2.name = 'research'\n", + "\n", + "# display(md('## Tweet Summary'))\n", + "summary = pd.DataFrame(sum1).join(sum2)\n", + "summary['multiplier'] = summary.news.divide(summary.research)\n", + "summary.to_clipboard()\n", + "display(md('### Comparing research and news on Twitter'))\n", + "summary" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## News Tweets" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### By Outlet" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_twnum_spaces_twnum_shares_twnum_likes_twnum_replies_twnum_spaces_tw_avgnum_shares_tw_avgnum_likes_tw_avgnum_replies_tw_avg
outlet
BBC News6280535788642345234820.851.413.730.55
MSN99941834180.950.180.340.18
New York Times6650240990299798854592673290.624.5112.851.01
The Guardian14033105746311812768976410.754.509.100.54
Washington Post10324745340712105695110400.723.9410.241.07
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" + ], + "text/plain": [ + " num_posts_tw num_spaces_tw num_shares_tw num_likes_tw \\\n", + "outlet \n", + "BBC News 6280 5357 8864 23452 \n", + "MSN 99 94 18 34 \n", + "New York Times 66502 40990 299798 854592 \n", + "The Guardian 14033 10574 63118 127689 \n", + "Washington Post 10324 7453 40712 105695 \n", + "\n", + " num_replies_tw num_spaces_tw_avg \\\n", + "outlet \n", + "BBC News 3482 0.85 \n", + "MSN 18 0.95 \n", + "New York Times 67329 0.62 \n", + "The Guardian 7641 0.75 \n", + "Washington Post 11040 0.72 \n", + "\n", + " num_shares_tw_avg num_likes_tw_avg \\\n", + "outlet \n", + "BBC News 1.41 3.73 \n", + "MSN 0.18 0.34 \n", + "New York Times 4.51 12.85 \n", + "The Guardian 4.50 9.10 \n", + "Washington Post 3.94 10.24 \n", + "\n", + " num_replies_tw_avg \n", + "outlet \n", + "BBC News 0.55 \n", + "MSN 0.18 \n", + "New York Times 1.01 \n", + "The Guardian 0.54 \n", + "Washington Post 1.07 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum correlation is 0.88\n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "### By Outlet per number of stories" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_twnum_spaces_twnum_shares_twnum_likes_twnum_replies_twNum Stories
outlet
BBC News114.1897.40165.40440.4267.4755
MSN2.252.140.430.910.4844
New York Times295.56182.181,659.004,854.77386.66225
The Guardian133.65100.70644.451,285.0577.37105
Washington Post181.12130.75754.911,938.28202.1657
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" + ], + "text/plain": [ + " num_posts_tw num_spaces_tw \\\n", + "outlet \n", + "BBC News 114.18 97.40 \n", + "MSN 2.25 2.14 \n", + "New York Times 295.56 182.18 \n", + "The Guardian 133.65 100.70 \n", + "Washington Post 181.12 130.75 \n", + "\n", + " num_shares_tw num_likes_tw \\\n", + "outlet \n", + "BBC News 165.40 440.42 \n", + "MSN 0.43 0.91 \n", + "New York Times 1,659.00 4,854.77 \n", + "The Guardian 644.45 1,285.05 \n", + "Washington Post 754.91 1,938.28 \n", + "\n", + " num_replies_tw Num Stories \n", + "outlet \n", + "BBC News 67.47 55 \n", + "MSN 0.48 44 \n", + "New York Times 386.66 225 \n", + "The Guardian 77.37 105 \n", + "Washington Post 202.16 57 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('## News Tweets'))\n", + "display(md('### By Outlet'))\n", + "\n", + "df = summarize_posts(single_count(news_tweets, 'outlet').groupby('outlet'), 'tw', True)\n", + "display(df)\n", + "m = summarize_posts(news_tweets.groupby('news_url'), 'tw', False).corr(method='spearman').min().min()\n", + "print(f'Minimum correlation is {m:.2f}')\n", + "print()\n", + "\n", + "display(md('### By Outlet per number of stories'))\n", + "normalize = news_tweets.groupby('outlet')['news_url'].nunique()\n", + "df = summarize_posts(news_tweets.groupby('outlet'), 'tw').divide(normalize, axis=0)\n", + "df['Num Stories'] = normalize\n", + "display(df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "### By Journal" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_twnum_spaces_twnum_shares_twnum_likes_twnum_replies_twnum_spaces_tw_avgnum_shares_tw_avgnum_likes_tw_avgnum_replies_tw_avg
journal
British Medical Journal182061399788391216415172910.774.8611.890.95
Journal of Medical Virology72935980183424017551640.822.525.510.71
bioRxiv203141430562770166440142050.703.098.190.70
medRxiv6122740359323585938969737170.665.2915.341.20
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" + ], + "text/plain": [ + " num_posts_tw num_spaces_tw num_shares_tw \\\n", + "journal \n", + "British Medical Journal 18206 13997 88391 \n", + "Journal of Medical Virology 7293 5980 18342 \n", + "bioRxiv 20314 14305 62770 \n", + "medRxiv 61227 40359 323585 \n", + "\n", + " num_likes_tw num_replies_tw \\\n", + "journal \n", + "British Medical Journal 216415 17291 \n", + "Journal of Medical Virology 40175 5164 \n", + "bioRxiv 166440 14205 \n", + "medRxiv 938969 73717 \n", + "\n", + " num_spaces_tw_avg \\\n", + "journal \n", + "British Medical Journal 0.77 \n", + "Journal of Medical Virology 0.82 \n", + "bioRxiv 0.70 \n", + "medRxiv 0.66 \n", + "\n", + " num_shares_tw_avg \\\n", + "journal \n", + "British Medical Journal 4.86 \n", + "Journal of Medical Virology 2.52 \n", + "bioRxiv 3.09 \n", + "medRxiv 5.29 \n", + "\n", + " num_likes_tw_avg \\\n", + "journal \n", + "British Medical Journal 11.89 \n", + "Journal of Medical Virology 5.51 \n", + "bioRxiv 8.19 \n", + "medRxiv 15.34 \n", + "\n", + " num_replies_tw_avg \n", + "journal \n", + "British Medical Journal 0.95 \n", + "Journal of Medical Virology 0.71 \n", + "bioRxiv 0.70 \n", + "medRxiv 1.20 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### By Journal per number of articles" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(first column is Average Posts Per Article)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_twnum_spaces_twnum_shares_twnum_likes_twnum_replies_twNum DOIs
journal
British Medical Journal233.41179.451,133.222,774.55221.6878
Journal of Medical Virology429.00351.761,078.942,363.24303.7617
bioRxiv677.13476.832,092.335,548.00473.5030
medRxiv816.36538.124,314.4712,519.59982.8975
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" + ], + "text/plain": [ + " num_posts_tw \\\n", + "journal \n", + "British Medical Journal 233.41 \n", + "Journal of Medical Virology 429.00 \n", + "bioRxiv 677.13 \n", + "medRxiv 816.36 \n", + "\n", + " num_spaces_tw \\\n", + "journal \n", + "British Medical Journal 179.45 \n", + "Journal of Medical Virology 351.76 \n", + "bioRxiv 476.83 \n", + "medRxiv 538.12 \n", + "\n", + " num_shares_tw \\\n", + "journal \n", + "British Medical Journal 1,133.22 \n", + "Journal of Medical Virology 1,078.94 \n", + "bioRxiv 2,092.33 \n", + "medRxiv 4,314.47 \n", + "\n", + " num_likes_tw \\\n", + "journal \n", + "British Medical Journal 2,774.55 \n", + "Journal of Medical Virology 2,363.24 \n", + "bioRxiv 5,548.00 \n", + "medRxiv 12,519.59 \n", + "\n", + " num_replies_tw Num DOIs \n", + "journal \n", + "British Medical Journal 221.68 78 \n", + "Journal of Medical Virology 303.76 17 \n", + "bioRxiv 473.50 30 \n", + "medRxiv 982.89 75 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print()\n", + "display(md('### By Journal'))\n", + "df = summarize_posts(news_tweets.groupby('journal'), 'tw', True)\n", + "display(df)\n", + "\n", + "\n", + "display(md('### By Journal per number of articles'))\n", + "display(md('(first column is Average Posts Per Article)'))\n", + "normalize = news_tweets.groupby('journal')['doi'].nunique()\n", + "df = summarize_posts(news_tweets.groupby('journal'), 'tw').divide(normalize, axis=0)\n", + "df['Num DOIs'] = normalize\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### By Outlet Num Posts Details" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Number of StoriesMean Numer of TweetsSt. Dev.Minimum25%50%75%MaximumTotal Tweets
outlet
BBC News55.00123.47254.321.008.5028.0097.501,102.006280
MSN44.002.955.781.001.001.002.0036.0099
New York Times225.00380.64994.821.0028.0073.00274.008,950.0066502
The Guardian105.00143.38241.922.0044.0072.00172.002,153.0014033
Washington Post57.00203.33286.271.0031.0090.00206.001,278.0010324
\n", + "
" + ], + "text/plain": [ + " Number of Stories Mean Numer of Tweets \\\n", + "outlet \n", + "BBC News 55.00 123.47 \n", + "MSN 44.00 2.95 \n", + "New York Times 225.00 380.64 \n", + "The Guardian 105.00 143.38 \n", + "Washington Post 57.00 203.33 \n", + "\n", + " St. Dev. Minimum \\\n", + "outlet \n", + "BBC News 254.32 1.00 \n", + "MSN 5.78 1.00 \n", + "New York Times 994.82 1.00 \n", + "The Guardian 241.92 2.00 \n", + "Washington Post 286.27 1.00 \n", + "\n", + " 25% 50% \\\n", + "outlet \n", + "BBC News 8.50 28.00 \n", + "MSN 1.00 1.00 \n", + "New York Times 28.00 73.00 \n", + "The Guardian 44.00 72.00 \n", + "Washington Post 31.00 90.00 \n", + "\n", + " 75% Maximum \\\n", + "outlet \n", + "BBC News 97.50 1,102.00 \n", + "MSN 2.00 36.00 \n", + "New York Times 274.00 8,950.00 \n", + "The Guardian 172.00 2,153.00 \n", + "Washington Post 206.00 1,278.00 \n", + "\n", + " Total Tweets \n", + "outlet \n", + "BBC News 6280 \n", + "MSN 99 \n", + "New York Times 66502 \n", + "The Guardian 14033 \n", + "Washington Post 10324 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md('### By Outlet Num Posts Details'))\n", + "df = news_tweets.groupby(['outlet', 'news_url']).size().reset_index().groupby('outlet')[0].describe()\n", + "df = df.join(news_tweets.groupby('outlet')['tweet_id'].nunique())\n", + "df.columns = ['Number of Stories', 'Mean Numer of Tweets', 'St. Dev.', 'Minimum', '25%', '50%', '75%', 'Maximum', 'Total Tweets']\n", + "df.to_clipboard()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top tweeted news" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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outletnews_urlmention_titlenum tweets
0New York Timeshttps://www.nytimes.com/interactive/2020/us/co...How the Virus Won4475
1New York Timeshttps://www.nytimes.com/2020/05/20/us/coronavi...Lockdown Delays Cost at Least 36,000 Lives, Da...4470
2New York Timeshttps://www.nytimes.com/2020/04/08/science/new...Most New York Coronavirus Cases Came From Euro...3876
3New York Timeshttps://www.nytimes.com/2020/11/17/health/coro...Immunity to the Coronavirus May Last Years, Ne...3289
4New York Timeshttps://www.nytimes.com/2020/08/11/health/coro...ëA Smoking Guní: Infectious Coronavirus Retrie...2633
5New York Timeshttps://www.nytimes.com/2020/12/20/health/coro...The Coronavirus Is Mutating. What Does That Me...2282
6The Guardianhttps://www.theguardian.com/commentisfree/2020...The government's secretive Covid contracts are...2153
7New York Timeshttps://www.nytimes.com/2020/05/15/world/coron...Why Are Women-Led Nations Doing Better With Co...2015
8New York Timeshttps://www.nytimes.com/2020/08/24/health/fda-...F.D.A. ëGrossly Misrepresentedí Blood Plasma D...1947
9New York Timeshttps://www.nytimes.com/2020/09/09/health/coro...How the Coronavirus Attacks the Brain1578
10New York Timeshttps://www.nytimes.com/2020/08/17/health/coro...What if ëHerd Immunityí Is Closer Than Scienti...1500
11New York Timeshttps://www.nytimes.com/2020/11/18/health/pfiz...New Pfizer Results: Coronavirus Vaccine Is Saf...1309
12New York Timeshttps://www.nytimes.com/2020/10/27/health/covi...Some Covid Survivors Have Antibodies That Atta...1283
13New York Timeshttps://www.nytimes.com/2020/08/16/health/coro...Scientists See Signs of Lasting Immunity to Co...1219
14New York Timeshttps://www.nytimes.com/interactive/2020/scien...Coronavirus Drug and Treatment Tracker1211
15New York Timeshttps://www.nytimes.com/interactive/2020/05/28...Nadja Popovich and Margot Sanger-Katz1186
16New York Timeshttps://www.nytimes.com/2020/03/17/opinion/cor...The Coronavirus Is Here to Stay, So What Happe...1132
17BBC Newshttps://www.bbc.com/future/article/20200622-th...The long-term effects of Covid-19 infection1102
18BBC Newshttps://www.bbc.com/news/health-55388846New coronavirus variant: What do we know?1091
19New York Timeshttps://www.nytimes.com/2020/04/01/health/coro...Some Coronavirus Patients Show Signs of Stroke...1081
\n", + "
" + ], + "text/plain": [ + " outlet news_url \\\n", + "0 New York Times https://www.nytimes.com/interactive/2020/us/co... \n", + "1 New York Times https://www.nytimes.com/2020/05/20/us/coronavi... \n", + "2 New York Times https://www.nytimes.com/2020/04/08/science/new... \n", + "3 New York Times https://www.nytimes.com/2020/11/17/health/coro... \n", + "4 New York Times https://www.nytimes.com/2020/08/11/health/coro... \n", + "5 New York Times https://www.nytimes.com/2020/12/20/health/coro... \n", + "6 The Guardian https://www.theguardian.com/commentisfree/2020... \n", + "7 New York Times https://www.nytimes.com/2020/05/15/world/coron... \n", + "8 New York Times https://www.nytimes.com/2020/08/24/health/fda-... \n", + "9 New York Times https://www.nytimes.com/2020/09/09/health/coro... \n", + "10 New York Times https://www.nytimes.com/2020/08/17/health/coro... \n", + "11 New York Times https://www.nytimes.com/2020/11/18/health/pfiz... \n", + "12 New York Times https://www.nytimes.com/2020/10/27/health/covi... \n", + "13 New York Times https://www.nytimes.com/2020/08/16/health/coro... \n", + "14 New York Times https://www.nytimes.com/interactive/2020/scien... \n", + "15 New York Times https://www.nytimes.com/interactive/2020/05/28... \n", + "16 New York Times https://www.nytimes.com/2020/03/17/opinion/cor... \n", + "17 BBC News https://www.bbc.com/future/article/20200622-th... \n", + "18 BBC News https://www.bbc.com/news/health-55388846 \n", + "19 New York Times https://www.nytimes.com/2020/04/01/health/coro... \n", + "\n", + " mention_title num tweets \n", + "0 How the Virus Won 4475 \n", + "1 Lockdown Delays Cost at Least 36,000 Lives, Da... 4470 \n", + "2 Most New York Coronavirus Cases Came From Euro... 3876 \n", + "3 Immunity to the Coronavirus May Last Years, Ne... 3289 \n", + "4 ëA Smoking Guní: Infectious Coronavirus Retrie... 2633 \n", + "5 The Coronavirus Is Mutating. What Does That Me... 2282 \n", + "6 The government's secretive Covid contracts are... 2153 \n", + "7 Why Are Women-Led Nations Doing Better With Co... 2015 \n", + "8 F.D.A. ëGrossly Misrepresentedí Blood Plasma D... 1947 \n", + "9 How the Coronavirus Attacks the Brain 1578 \n", + "10 What if ëHerd Immunityí Is Closer Than Scienti... 1500 \n", + "11 New Pfizer Results: Coronavirus Vaccine Is Saf... 1309 \n", + "12 Some Covid Survivors Have Antibodies That Atta... 1283 \n", + "13 Scientists See Signs of Lasting Immunity to Co... 1219 \n", + "14 Coronavirus Drug and Treatment Tracker 1211 \n", + "15 Nadja Popovich and Margot Sanger-Katz 1186 \n", + "16 The Coronavirus Is Here to Stay, So What Happe... 1132 \n", + "17 The long-term effects of Covid-19 infection 1102 \n", + "18 New coronavirus variant: What do we know? 1091 \n", + "19 Some Coronavirus Patients Show Signs of Stroke... 1081 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md(\"### Top tweeted news\"))\n", + "df = single_count(news_tweets, 'news_url')\n", + "df = df.groupby(['outlet', 'news_url', 'mention_title']).size().nlargest(20).reset_index()\n", + "df.columns = ['outlet', 'news_url', 'mention_title', 'num tweets']\n", + "df.style.format({'news_url': make_clickable, \n", + " 'num tweets': lambda x: \"{:,}\".format(x)})\n", + "df.to_clipboard()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top tweeted research by news story" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(the research whose stories were tweeted the most)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
outlet doi article_title journal BBC News MSN New York Times The Guardian Washington Post Total
010.1101/2020.05.15.20103655Differential Effects of Intervention Timing on COVID-19 Spread in the United StatesmedRxiv61111,2957347211,902
110.1101/2020.04.16.20065920Outcomes of hydroxychloroquine usage in United States veterans hospitalized with Covid-19medRxiv004,1034921,1595,754
210.1101/2020.08.11.20171843Functional SARS-CoV-2-specific immune memory persists after mild COVID-19medRxiv094,7320554,796
310.1101/2020.11.15.383323Immunological memory to SARS-CoV-2 assessed for up to eight months after infectionbioRxiv084,3789104,477
410.1101/2020.04.15.20064931Sequencing identifies multiple early introductions of SARS-CoV-2 to the New York City RegionmedRxiv004,475004,475
510.1101/2020.03.25.20043828Coast-to-coast spread of SARS-CoV-2 in the United States revealed by genomic epidemiologymedRxiv003,876003,876
610.1101/2020.08.03.20167395Viable SARS-CoV-2 in the air of a hospital room with COVID-19 patientsmedRxiv043,32102623,587
710.1136/bmj.m2486Covid-19: Local health teams trace eight times more contacts than national serviceBritish Medical Journal0002,53402,534
810.1101/2020.08.12.20169359Effect of Convalescent Plasma on Mortality among Hospitalized Patients with COVID-19: Initial Three-Month ExperiencemedRxiv0102,37601342,520
910.1101/2020.12.05.20241927Neutralising antibodies in Spike mediated SARS-CoV-2 adaptationmedRxiv902,35501342,498
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NB: single_count is not necessary here because already unique by DOI\n", + "\n", + "display(md(\"### Top tweeted research by news story\"))\n", + "display(md('(the research whose stories were tweeted the most)'))\n", + "top_dois = single_count(news_tweets, 'doi').groupby('doi').size().nlargest(10).index\n", + "\n", + "df = single_count(news_tweets[news_tweets.doi.isin(top_dois)], 'doi').groupby(['doi', 'article_title', 'journal', 'outlet']).size().unstack().fillna(0)\n", + "df['Total'] = df.sum(axis=1)\n", + "\n", + "df = df.sort_values('Total', ascending=False).applymap(lambda x: \"{:,.0f}\".format(x)).reset_index() \n", + "df.to_clipboard()\n", + "df.style.format({'doi': make_doi_clickable})\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Research Tweets" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### By Journal" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Journal of Medical Virology751610347278756760.814.6210.490.90
bioRxiv1725125794933132727260.735.5018.161.58
medRxiv54523755255096825456940.694.6812.521.04
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num_replies_tw0.950.950.930.951.00
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" + ], + "text/plain": [ + " num_posts_tw num_spaces_tw \\\n", + "num_posts_tw 1.00 1.00 \n", + "num_spaces_tw 1.00 1.00 \n", + "num_shares_tw 0.91 0.92 \n", + "num_likes_tw 0.92 0.92 \n", + "num_replies_tw 0.95 0.95 \n", + "\n", + " num_shares_tw num_likes_tw \\\n", + "num_posts_tw 0.91 0.92 \n", + "num_spaces_tw 0.92 0.92 \n", + "num_shares_tw 1.00 0.98 \n", + "num_likes_tw 0.98 1.00 \n", + "num_replies_tw 0.93 0.95 \n", + "\n", + " num_replies_tw \n", + "num_posts_tw 0.95 \n", + "num_spaces_tw 0.95 \n", + "num_shares_tw 0.93 \n", + "num_likes_tw 0.95 \n", + "num_replies_tw 1.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum correlation is 0.91\n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "### By Journal per number of articles" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(first column is Average Posts Per Article)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_twnum_spaces_twnum_shares_twnum_likes_twnum_replies_twNum DOIs
journal
British Medical Journal288.26159.241,301.092,811.21215.50147
Journal of Medical Virology28.8823.46133.54302.8826.0026
bioRxiv41.0729.93226.02745.8864.9042
medRxiv49.5634.14231.90620.4951.76110
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" + ], + "text/plain": [ + " num_posts_tw \\\n", + "journal \n", + "British Medical Journal 288.26 \n", + "Journal of Medical Virology 28.88 \n", + "bioRxiv 41.07 \n", + "medRxiv 49.56 \n", + "\n", + " num_spaces_tw \\\n", + "journal \n", + "British Medical Journal 159.24 \n", + "Journal of Medical Virology 23.46 \n", + "bioRxiv 29.93 \n", + "medRxiv 34.14 \n", + "\n", + " num_shares_tw \\\n", + "journal \n", + "British Medical Journal 1,301.09 \n", + "Journal of Medical Virology 133.54 \n", + "bioRxiv 226.02 \n", + "medRxiv 231.90 \n", + "\n", + " num_likes_tw \\\n", + "journal \n", + "British Medical Journal 2,811.21 \n", + "Journal of Medical Virology 302.88 \n", + "bioRxiv 745.88 \n", + "medRxiv 620.49 \n", + "\n", + " num_replies_tw Num DOIs \n", + "journal \n", + "British Medical Journal 215.50 147 \n", + "Journal of Medical Virology 26.00 26 \n", + "bioRxiv 64.90 42 \n", + "medRxiv 51.76 110 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('## Research Tweets'))\n", + "display(md('### By Journal'))\n", + "display(summarize_posts(research_tweets.groupby('journal'), 'tw', True))\n", + "m = summarize_posts(research_tweets.groupby('doi'), 'tw', False).corr(method='spearman').min().min()\n", + "display(summarize_posts(research_tweets.groupby('doi'), 'tw', False).corr(method='spearman'))\n", + "print(f'Minimum correlation is {m:.2f}')\n", + "print()\n", + "\n", + "display(md('### By Journal per number of articles'))\n", + "display(md('(first column is Average Posts Per Article)'))\n", + "normalize = research_tweets.groupby('journal')['doi'].nunique()\n", + "df = summarize_posts(research_tweets.groupby('journal'), 'tw').divide(normalize, axis=0)\n", + "df['Num DOIs'] = normalize\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top tweeted articles" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
doi num tweets
010.1136/bmj.m44255,542
110.1136/bmj.m40373,676
210.1136/bmj.m32232,309
310.1136/bmj.m35632,154
410.1136/bmj.m19321,973
510.1136/bmj.m48571,431
610.1136/bmj.m13751,127
710.1136/bmj.m18491,112
810.1136/bmj.m19311,070
910.1136/bmj.m18081,001
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md(\"### Top tweeted articles\"))\n", + "df = research_tweets.groupby('doi').size().nlargest(10).reset_index()\n", + "df.columns = ['doi', 'num tweets']\n", + "df.style.format({'doi': make_doi_clickable, \n", + " 'num tweets': lambda x: \"{:,}\".format(x)})" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top tweeted research by news compared to top tweeted research" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(the research whose stories were tweeted the most compared to the research that was tweeted the most)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# add in the rank of the DOI in terms of how often it is tweeted on its own\n", + "display(md(\"### Top tweeted research by news compared to top tweeted research\"))\n", + "display(md('(the research whose stories were tweeted the most compared to the research that was tweeted the most)'))\n", + "\n", + "df = single_count(news_tweets, 'doi').groupby('doi').size().nlargest(10)\n", + "df = df.to_frame()\n", + "df.columns = ['num_news_tweets']\n", + "df['rank_news'] = df['num_news_tweets'].rank(ascending=False, method='min').astype(int)\n", + "df = df.join(news_tweets[['doi', 'article_title']].drop_duplicates().set_index('doi'))\n", + "\n", + "df2 = research_tweets.groupby('doi').size().to_frame()\n", + "df2.columns = ['num_research_tweets']\n", + "df2['rank_research'] = df2['num_research_tweets'].rank(ascending=False, method='min').astype(int)\n", + "df = df.join(df2)\n", + "df = df[['article_title', 'num_news_tweets', 'rank_news', 'num_research_tweets', 'rank_research']]\n", + "\n", + "df.to_clipboard()\n", + "\n", + "df = df.reset_index()\n", + "df.style.format({'doi': make_doi_clickable, \n", + " 'num tweets': lambda x: \"{:,}\".format(x)})\n", + "\n", + "twitter_top_10 = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Facebook Summary" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Comparing research and news on Facebook" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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newsresearchmultiplier
num_posts_fb14,083.06,420.02.2
num_spaces_fb8,193.03,976.02.1
num_shares_fb412,114.089,422.04.6
num_likes_fb1,476,264.0176,890.08.3
num_replies_fb304,633.036,203.08.4
num_spaces_fb_avg0.60.60.9
num_shares_fb_avg29.313.92.1
num_likes_fb_avg104.827.63.8
num_replies_fb_avg21.65.63.8
\n", + "
" + ], + "text/plain": [ + " news research multiplier\n", + "num_posts_fb 14,083.0 6,420.0 2.2\n", + "num_spaces_fb 8,193.0 3,976.0 2.1\n", + "num_shares_fb 412,114.0 89,422.0 4.6\n", + "num_likes_fb 1,476,264.0 176,890.0 8.3\n", + "num_replies_fb 304,633.0 36,203.0 8.4\n", + "num_spaces_fb_avg 0.6 0.6 0.9\n", + "num_shares_fb_avg 29.3 13.9 2.1\n", + "num_likes_fb_avg 104.8 27.6 3.8\n", + "num_replies_fb_avg 21.6 5.6 3.8" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fb_cols = ['num_posts_fb', 'num_spaces_fb', 'num_reactions_fb', 'num_shares_fb', 'num_comments_fb']\n", + "\n", + "def summarize_fb(df):\n", + " tmp = df[['platformId', 'any_reaction', 'actualShareCount', 'actualCommentCount', 'accountId']] \\\n", + " .agg({'platformId': 'size', \n", + " 'accountId': 'nunique',\n", + " 'any_reaction': 'sum', \n", + " 'actualShareCount': 'sum',\n", + " 'actualCommentCount': 'sum'})\n", + " if type(tmp) == pd.Series:\n", + " tmp.index = fb_cols\n", + " else:\n", + " tmp.columns = fb_cols\n", + " return tmp\n", + "\n", + "sum1 = summarize_posts(single_count(news_fb), 'fb', True)\n", + "sum1.name = 'news'\n", + "\n", + "\n", + "sum2 = summarize_posts(single_count(research_fb), 'fb', True)\n", + "sum2.name = 'research'\n", + "\n", + "display(md('## Facebook Summary'))\n", + "summary = pd.DataFrame(sum1).join(sum2)\n", + "summary['multiplier'] = summary.news.divide(summary.research)\n", + "summary.to_clipboard()\n", + "display(md('### Comparing research and news on Facebook'))\n", + "summary.applymap(lambda x: \"{:,.1f}\".format(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## News FB Posts" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### By Outlet" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbnum_spaces_fb_avgnum_shares_fb_avgnum_likes_fb_avgnum_replies_fb_avg
outlet
BBC News1885151047317155545373980.8025.1082.5219.84
MSN288180456718549135670.6215.8664.4147.11
New York Times7490480035990615328762530210.6448.05204.6633.78
The Guardian2943176644120108379270000.6014.9936.839.17
Washington Post1477107757412175654440730.7338.87118.9329.84
\n", + "
" + ], + "text/plain": [ + " num_posts_fb num_spaces_fb num_shares_fb num_likes_fb \\\n", + "outlet \n", + "BBC News 1885 1510 47317 155545 \n", + "MSN 288 180 4567 18549 \n", + "New York Times 7490 4800 359906 1532876 \n", + "The Guardian 2943 1766 44120 108379 \n", + "Washington Post 1477 1077 57412 175654 \n", + "\n", + " num_replies_fb num_spaces_fb_avg \\\n", + "outlet \n", + "BBC News 37398 0.80 \n", + "MSN 13567 0.62 \n", + "New York Times 253021 0.64 \n", + "The Guardian 27000 0.60 \n", + "Washington Post 44073 0.73 \n", + "\n", + " num_shares_fb_avg num_likes_fb_avg \\\n", + "outlet \n", + "BBC News 25.10 82.52 \n", + "MSN 15.86 64.41 \n", + "New York Times 48.05 204.66 \n", + "The Guardian 14.99 36.83 \n", + "Washington Post 38.87 118.93 \n", + "\n", + " num_replies_fb_avg \n", + "outlet \n", + "BBC News 19.84 \n", + "MSN 47.11 \n", + "New York Times 33.78 \n", + "The Guardian 9.17 \n", + "Washington Post 29.84 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Minimum correlation is 0.75\n", + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "### By Outlet per number of stories" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbNum Stories
outlet
BBC News27.3221.88685.752,254.28542.0069
MSN2.291.4336.25147.21107.67126
New York Times46.8130.002,249.419,580.481,581.38160
The Guardian27.7616.66416.231,022.44254.72106
Washington Post26.8519.581,043.853,193.71801.3355
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" + ], + "text/plain": [ + " num_posts_fb num_spaces_fb \\\n", + "outlet \n", + "BBC News 27.32 21.88 \n", + "MSN 2.29 1.43 \n", + "New York Times 46.81 30.00 \n", + "The Guardian 27.76 16.66 \n", + "Washington Post 26.85 19.58 \n", + "\n", + " num_shares_fb num_likes_fb \\\n", + "outlet \n", + "BBC News 685.75 2,254.28 \n", + "MSN 36.25 147.21 \n", + "New York Times 2,249.41 9,580.48 \n", + "The Guardian 416.23 1,022.44 \n", + "Washington Post 1,043.85 3,193.71 \n", + "\n", + " num_replies_fb Num Stories \n", + "outlet \n", + "BBC News 542.00 69 \n", + "MSN 107.67 126 \n", + "New York Times 1,581.38 160 \n", + "The Guardian 254.72 106 \n", + "Washington Post 801.33 55 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('## News FB Posts'))\n", + "display(md('### By Outlet'))\n", + "display(summarize_posts(news_fb.groupby('outlet'), 'fb', True))\n", + "m = summarize_posts(news_fb.groupby('news_url'), 'fb', False).corr(method='spearman').min().min()\n", + "print(f'Minimum correlation is {m:.2f}')\n", + "print()\n", + "\n", + "\n", + "display(md('### By Outlet per number of stories'))\n", + "normalize = news_fb.groupby('outlet')['news_url'].nunique()\n", + "df = summarize_posts(news_fb.groupby('outlet'), 'fb').divide(normalize, axis=0)\n", + "df['Num Stories'] = normalize\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### By Outlet Num Posts Details" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Number of StoriesMean Numer of FB PostsSt. Dev.Minimum25%50%75%MaximumTotal FB Posts
outlet
BBC News69.0029.0052.771.005.008.0027.00339.001885
MSN126.002.673.721.001.001.003.0034.00288
New York Times160.0065.40147.701.006.0018.0051.251,112.007490
The Guardian106.0029.6248.591.006.0012.5031.50323.002943
Washington Post55.0035.6568.631.004.5011.0035.00406.001477
\n", + "
" + ], + "text/plain": [ + " Number of Stories Mean Numer of FB Posts \\\n", + "outlet \n", + "BBC News 69.00 29.00 \n", + "MSN 126.00 2.67 \n", + "New York Times 160.00 65.40 \n", + "The Guardian 106.00 29.62 \n", + "Washington Post 55.00 35.65 \n", + "\n", + " St. Dev. Minimum \\\n", + "outlet \n", + "BBC News 52.77 1.00 \n", + "MSN 3.72 1.00 \n", + "New York Times 147.70 1.00 \n", + "The Guardian 48.59 1.00 \n", + "Washington Post 68.63 1.00 \n", + "\n", + " 25% 50% \\\n", + "outlet \n", + "BBC News 5.00 8.00 \n", + "MSN 1.00 1.00 \n", + "New York Times 6.00 18.00 \n", + "The Guardian 6.00 12.50 \n", + "Washington Post 4.50 11.00 \n", + "\n", + " 75% Maximum \\\n", + "outlet \n", + "BBC News 27.00 339.00 \n", + "MSN 3.00 34.00 \n", + "New York Times 51.25 1,112.00 \n", + "The Guardian 31.50 323.00 \n", + "Washington Post 35.00 406.00 \n", + "\n", + " Total FB Posts \n", + "outlet \n", + "BBC News 1885 \n", + "MSN 288 \n", + "New York Times 7490 \n", + "The Guardian 2943 \n", + "Washington Post 1477 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md('### By Outlet Num Posts Details'))\n", + "df = news_fb.groupby(['outlet', 'news_url']).size().reset_index().groupby('outlet')[0].describe()\n", + "df = df.join(news_fb.groupby('outlet')['platformId'].nunique())\n", + "df.columns = ['Number of Stories', 'Mean Numer of FB Posts', 'St. Dev.', 'Minimum', '25%', '50%', '75%', 'Maximum', 'Total FB Posts']\n", + "df.to_clipboard()\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### By Journal" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbnum_spaces_fb_avgnum_shares_fb_avgnum_likes_fb_avgnum_replies_fb_avg
journal
British Medical Journal34002166104333335200637900.6430.6998.5918.76
Journal of Medical Virology99887527158103319136720.8827.21103.5313.70
bioRxiv2895212366230402411607330.7322.88139.0020.98
medRxiv8103535231560111500732368640.6638.95141.9329.23
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" + ], + "text/plain": [ + " num_posts_fb num_spaces_fb num_shares_fb \\\n", + "journal \n", + "British Medical Journal 3400 2166 104333 \n", + "Journal of Medical Virology 998 875 27158 \n", + "bioRxiv 2895 2123 66230 \n", + "medRxiv 8103 5352 315601 \n", + "\n", + " num_likes_fb num_replies_fb \\\n", + "journal \n", + "British Medical Journal 335200 63790 \n", + "Journal of Medical Virology 103319 13672 \n", + "bioRxiv 402411 60733 \n", + "medRxiv 1150073 236864 \n", + "\n", + " num_spaces_fb_avg \\\n", + "journal \n", + "British Medical Journal 0.64 \n", + "Journal of Medical Virology 0.88 \n", + "bioRxiv 0.73 \n", + "medRxiv 0.66 \n", + "\n", + " num_shares_fb_avg \\\n", + "journal \n", + "British Medical Journal 30.69 \n", + "Journal of Medical Virology 27.21 \n", + "bioRxiv 22.88 \n", + "medRxiv 38.95 \n", + "\n", + " num_likes_fb_avg \\\n", + "journal \n", + "British Medical Journal 98.59 \n", + "Journal of Medical Virology 103.53 \n", + "bioRxiv 139.00 \n", + "medRxiv 141.93 \n", + "\n", + " num_replies_fb_avg \n", + "journal \n", + "British Medical Journal 18.76 \n", + "Journal of Medical Virology 13.70 \n", + "bioRxiv 20.98 \n", + "medRxiv 29.23 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/markdown": [ + "### By Journal per number of articles" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbNum DOIs
journal
British Medical Journal38.6424.611,185.603,809.09724.8988
Journal of Medical Virology47.5241.671,293.244,919.95651.0521
bioRxiv93.3968.482,136.4512,981.001,959.1331
medRxiv106.6270.424,152.6415,132.543,116.6376
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" + ], + "text/plain": [ + " num_posts_fb \\\n", + "journal \n", + "British Medical Journal 38.64 \n", + "Journal of Medical Virology 47.52 \n", + "bioRxiv 93.39 \n", + "medRxiv 106.62 \n", + "\n", + " num_spaces_fb \\\n", + "journal \n", + "British Medical Journal 24.61 \n", + "Journal of Medical Virology 41.67 \n", + "bioRxiv 68.48 \n", + "medRxiv 70.42 \n", + "\n", + " num_shares_fb \\\n", + "journal \n", + "British Medical Journal 1,185.60 \n", + "Journal of Medical Virology 1,293.24 \n", + "bioRxiv 2,136.45 \n", + "medRxiv 4,152.64 \n", + "\n", + " num_likes_fb \\\n", + "journal \n", + "British Medical Journal 3,809.09 \n", + "Journal of Medical Virology 4,919.95 \n", + "bioRxiv 12,981.00 \n", + "medRxiv 15,132.54 \n", + "\n", + " num_replies_fb Num DOIs \n", + "journal \n", + "British Medical Journal 724.89 88 \n", + "Journal of Medical Virology 651.05 21 \n", + "bioRxiv 1,959.13 31 \n", + "medRxiv 3,116.63 76 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('### By Journal'))\n", + "display(summarize_posts(news_fb.groupby('journal'), 'fb', True))\n", + "print()\n", + "\n", + "display(md('### By Journal per number of articles'))\n", + "normalize = news_fb.groupby('journal')['doi'].nunique()\n", + "df = summarize_posts(news_fb.groupby('journal'), 'fb').divide(normalize, axis=0)\n", + "df['Num DOIs'] = normalize\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top FB Posted research by news story" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(the research whose stories were posted on FB the most)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
outlet doi BBC News MSN New York Times The Guardian Washington Post Total
010.1101/2020.05.15.2010365516101,13112461,215
110.1101/2020.08.11.2017184302375809790
210.1101/2020.04.16.200659200846272186728
310.1101/2020.11.15.383323018597250640
410.1101/2020.08.03.20167395012471038521
510.1136/bmj.m24860004300430
610.1101/2020.04.15.200649310040900409
710.1101/2020.09.22.201991250200406408
810.1101/2020.03.09.20033217346140320392
910.1101/2020.04.05.2005450289002546349
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md(\"### Top FB Posted research by news story\"))\n", + "display(md('(the research whose stories were posted on FB the most)'))\n", + "top_dois = news_fb.groupby('doi').size().nlargest(10).index\n", + "\n", + "df = news_fb[news_fb.doi.isin(top_dois)].groupby(['doi', 'outlet']).size().unstack().fillna(0)\n", + "df['Total'] = df.sum(axis=1)\n", + "df = df.sort_values('Total', ascending=False).applymap(lambda x: \"{:,.0f}\".format(x)).reset_index()\n", + "df.style.format({'doi': make_doi_clickable})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Research FB Posts" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### By Journal" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbnum_spaces_fb_avgnum_shares_fb_avgnum_likes_fb_avgnum_replies_fb_avg
journal
British Medical Journal5363333571482132100312470.6213.3324.635.83
Journal of Medical Virology137126115523204640.928.4316.933.39
bioRxiv2211901765683411960.867.9930.925.41
medRxiv700594166543749545150.8523.7953.566.45
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num_posts_fbnum_spaces_fbnum_shares_fbnum_likes_fbnum_replies_fbNum DOIs
journal
British Medical Journal39.1524.34521.77964.23228.08137
Journal of Medical Virology10.549.6988.85178.4635.6913
bioRxiv7.896.7963.04244.0742.7128
medRxiv10.298.74244.91551.4066.4068
\n", + "
" + ], + "text/plain": [ + " num_posts_fb \\\n", + "journal \n", + "British Medical Journal 39.15 \n", + "Journal of Medical Virology 10.54 \n", + "bioRxiv 7.89 \n", + "medRxiv 10.29 \n", + "\n", + " num_spaces_fb \\\n", + "journal \n", + "British Medical Journal 24.34 \n", + "Journal of Medical Virology 9.69 \n", + "bioRxiv 6.79 \n", + "medRxiv 8.74 \n", + "\n", + " num_shares_fb \\\n", + "journal \n", + "British Medical Journal 521.77 \n", + "Journal of Medical Virology 88.85 \n", + "bioRxiv 63.04 \n", + "medRxiv 244.91 \n", + "\n", + " num_likes_fb \\\n", + "journal \n", + "British Medical Journal 964.23 \n", + "Journal of Medical Virology 178.46 \n", + "bioRxiv 244.07 \n", + "medRxiv 551.40 \n", + "\n", + " num_replies_fb Num DOIs \n", + "journal \n", + "British Medical Journal 228.08 137 \n", + "Journal of Medical Virology 35.69 13 \n", + "bioRxiv 42.71 28 \n", + "medRxiv 66.40 68 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('## Research FB Posts'))\n", + "display(md('### By Journal'))\n", + "display(summarize_posts(research_fb.groupby('journal'), 'fb', True))\n", + "m = summarize_posts(research_fb.groupby('doi'), 'fb', False).corr(method='spearman').min().min()\n", + "print(f'Minimum correlation is {m:.2f}')\n", + "\n", + "\n", + "display(md('### By Journal per number of articles'))\n", + "normalize = research_fb.groupby('journal')['doi'].nunique()\n", + "df = summarize_posts(research_fb.groupby('journal'), 'fb').divide(normalize, axis=0)\n", + "df['Num DOIs'] = normalize\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top posted articles" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
doi num posts
010.1136/bmj.m4425906
110.1136/bmj.m4037574
210.1136/bmj.m3223350
310.1136/bmj.m1435306
410.1136/bmj.m1375285
510.1136/bmj.m3563245
610.1136/bmj.m1932242
710.1136/bmj.m4857187
810.1136/bmj.m1808132
910.1136/bmj.m1931120
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md(\"### Top posted articles\"))\n", + "df = research_fb.groupby('doi').size().nlargest(10).reset_index()\n", + "df.columns = ['doi', 'num posts']\n", + "df.style.format({'doi': make_doi_clickable})" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Top FB research by news compared to top tweeted research" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(the research whose stories were most posted on FB the most compared to the research that was posted on FB the most)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# add in the rank of the DOI in terms of how often it is tweeted on its own\n", + "display(md(\"### Top FB research by news compared to top tweeted research\"))\n", + "display(md('(the research whose stories were most posted on FB the most compared to the research that was posted on FB the most)'))\n", + "\n", + "df = single_count(news_fb, 'doi').groupby('doi').size().nlargest(10)\n", + "df = df.to_frame()\n", + "df.columns = ['num_news_fb']\n", + "df['rank_news'] = df['num_news_fb'].rank(ascending=False, method='min').astype(int)\n", + "df = df.join(news_tweets[['doi', 'article_title']].drop_duplicates().set_index('doi'))\n", + "\n", + "df2 = research_fb.groupby('doi').size().to_frame()\n", + "df2.columns = ['num_research_fb']\n", + "df2['rank_research'] = df2['num_research_fb'].rank(ascending=False, method='min')\n", + "df = df.join(df2).fillna(0)\n", + "df['rank_research'] = df['rank_research'].astype(int)\n", + "df = df[['article_title', 'num_news_fb', 'rank_news', 'num_research_fb', 'rank_research']]\n", + "df['rank_research'] = df['rank_research'].astype(int)\n", + "df['num_research_fb'] = df['num_research_fb'].astype(int)\n", + "\n", + "\n", + "df.to_clipboard()\n", + "\n", + "df = df.reset_index()\n", + "\n", + "df.style.format({'doi': make_doi_clickable, \n", + " 'num_news_fb': lambda x: \"{:,}\".format(x),\n", + " 'num_research_fb': lambda x: \"{:,}\".format(x)})\n", + "\n", + "fb_top_10 = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "nt_cnts = single_count(news_tweets, 'doi').groupby('doi').size().to_frame()\n", + "nt_cnts.columns = ['news_tweets']\n", + "rt_cnts = research_tweets.groupby('doi').size().to_frame()\n", + "rt_cnts.columns = ['research_tweets']\n", + "\n", + "nfb_cnts = single_count(news_fb, 'doi').groupby('doi').size().to_frame()\n", + "nfb_cnts.columns = ['news_fb']\n", + "rfb_cnts = research_fb.groupby('doi').size().to_frame()\n", + "rfb_cnts.columns = ['research_fb']\n", + "\n", + "for df in [nt_cnts, rt_cnts, nfb_cnts, rfb_cnts]:\n", + " for c in df.columns:\n", + " df[c + '_rank'] = df[c].rank(ascending=False, method='min').astype(int)\n", + "\n", + "df = news_tweets[['doi', 'article_title']].drop_duplicates().set_index('doi')\n", + "df = df.join(nt_cnts).join(nfb_cnts, how=\"outer\").join(rt_cnts, how=\"outer\").join(rfb_cnts, how=\"outer\")\n", + "df = df[(df.news_tweets_rank <= 10) | (df.news_fb_rank <= 10)]\n", + "df = df.sort_values('news_tweets_rank').fillna(0)\n", + "df.to_clipboard()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "by_doi_tw = summarize_posts(news_tweets.groupby('doi'), 'tw').merge(summarize_posts(research_tweets.groupby('doi'), 'tw'), how='outer', left_index=True, right_index=True, suffixes=['_news', '_research']).fillna(0).astype(int)\n", + "by_doi_fb = summarize_posts(news_fb.groupby('doi'), 'fb').merge(summarize_posts(research_fb.groupby('doi'), 'fb'), how='outer', left_index=True, right_index=True, suffixes=['_news', '_research']).fillna(0).astype(int)\n", + "by_doi = by_doi_tw.merge(by_doi_fb, how='outer', left_index=True, right_index=True)\n", + "\n", + "main_cols = ['num_posts_%s_%s' % (p,c) for p in ['tw', 'fb'] for c in ['research', 'news']]\n", + "\n", + "by_doi_main = by_doi[main_cols]\n", + "# display(by_doi)\n", + "\n", + "def make_wordy_col_names(c): \n", + " if 'research' in c: \n", + " ret = 'First-order %s (Research)'\n", + " elif 'news' in c:\n", + " ret = 'Second-order %s (News)'\n", + " else:\n", + " return \"Bad Column Name\"\n", + " \n", + " if 'tw' in c:\n", + " return ret % 'Twitter'\n", + " elif 'fb' in c:\n", + " return ret % 'Facebook'\n", + "\n", + "by_doi_main.columns = [make_wordy_col_names(c) for c in by_doi_main.columns]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " First-order Twitter (Research) \\\n", + "First-order Twitter (Research) 1.0000 \n", + "Second-order Twitter (News) 0.0157 \n", + "First-order Facebook (Research) 0.0000 \n", + "Second-order Facebook (News) 0.7059 \n", + "\n", + " Second-order Twitter (News) \\\n", + "First-order Twitter (Research) 0.0157 \n", + "Second-order Twitter (News) 1.0000 \n", + "First-order Facebook (Research) 0.8720 \n", + "Second-order Facebook (News) 0.0000 \n", + "\n", + " First-order Facebook (Research) \\\n", + "First-order Twitter (Research) 0.0000 \n", + "Second-order Twitter (News) 0.8720 \n", + "First-order Facebook (Research) 1.0000 \n", + "Second-order Facebook (News) 0.9440 \n", + "\n", + " Second-order Facebook (News) \n", + "First-order Twitter (Research) 0.7059 \n", + "Second-order Twitter (News) 0.0000 \n", + "First-order Facebook (Research) 0.9440 \n", + "Second-order Facebook (News) 1.0000 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.stats import spearmanr\n", + "\n", + "def spearmanr_pval(x,y):\n", + " return spearmanr(x,y)[1]\n", + "\n", + "display(md(\"## Comparing DOIs across outlets and journals\"))\n", + "display(md(\"### Correlations in number of posts of research and news across Twitter and FB\"))\n", + "\n", + "display(md('''\n", + "tweets of news correlated with FB of news, and tweets of research with FB of research\n", + "\n", + "news and research not correlated on either platform\n", + "'''))\n", + "\n", + "corr = by_doi_main.corr(method='spearman')\n", + "# corr.to_clipboard()\n", + "display(md('**Correlations**'))\n", + "display(corr)\n", + "corr.to_clipboard()\n", + "print()\n", + "print()\n", + "p_s = by_doi_main.corr(method=spearmanr_pval)\n", + "display(md('**p-values**'))\n", + "display(p_s.applymap(lambda x: \"{:,.4f}\".format(x)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display(md(\"### Comparing number of posts of research and news across Twitter and FB\"))\n", + "# display(md(\"each dot is a DOI\"))\n", + "\n", + "sns.set(font_scale=1.4)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "def hide_current_axis(*args, **kwds):\n", + " plt.gca().set_visible(False)\n", + "\n", + "df = by_doi_main.copy()\n", + "def add_newlines_to_cols(c):\n", + " c.split\n", + "\n", + "df.columns = ['\\n'.join(c.split()) for c in df.columns]\n", + "# df.rename(columns = {'num_posts_tw_news': 'Second Order\\nTweets\\n(News)', \n", + "# 'num_posts_tw_research': 'First Order\\nTweets\\n(Research)', \n", + "# 'num_posts_fb_news': 'Second Order\\nFacebook Posts\\n(News)',\n", + "# 'num_posts_fb_research': 'First Order\\nFacebook Posts\\n(Research)'}, inplace=True)\n", + "\n", + "plt.rcParams['figure.figsize']=18,18\n", + "g = sns.pairplot(df.merge(articles[['doi', 'journal']].set_index('doi'), left_index=True, right_index=True), \n", + " hue='journal', \n", + " plot_kws={'alpha': 0.7},\n", + " corner=True\n", + " )\n", + "\n", + "g.fig.subplots_adjust(bottom=0.15, left=0.1, right=.95)\n", + "\n", + "handles = g._legend_data.values()\n", + "labels = g._legend_data.keys()\n", + "g._legend.remove()\n", + "\n", + "# g.map_upper(hide_current_axis)\n", + "\n", + "# g.fig.legend(handles=handles, labels=labels, loc='upper center', ncol=1)\n", + "g.fig.legend(handles=handles, labels=labels, loc='upper right', ncol=1, frameon=False, fontsize=16, bbox_to_anchor=(1, .9))\n", + "# g._legend.set_bbox_to_anchor((1, 1))\n", + "\n", + "# g.fig.legend(handles=handles, labels=labels, loc='lower center', ncol=len(labels), frameon=False, fontsize=14)\n", + "# g._legend.set_bbox_to_anchor((0.5, 0))\n", + "\n", + "\n", + "plt.savefig('figures/pairplot.png')\n", + "\n", + "\n", + "None" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "by_journal_tw = summarize_posts(news_tweets.groupby('journal'), 'tw').merge(summarize_posts(research_tweets.groupby('journal'), 'tw'), how='outer', left_index=True, right_index=True, suffixes=['_news', '_research']).fillna(0).astype(int)\n", + "by_journal_fb = summarize_posts(news_fb.groupby('journal'), 'fb').merge(summarize_posts(research_fb.groupby('journal'), 'fb'), how='outer', left_index=True, right_index=True, suffixes=['_news', '_research']).fillna(0).astype(int)\n", + "by_journal = by_journal_tw.merge(by_journal_fb, how='outer', left_index=True, right_index=True)\n", + "\n", + "by_journal_main = by_journal[main_cols]\n", + "# display(by_doi)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Summary of number of posts on Twitter and FB: by DOI" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " First-order Twitter (Research) Second-order Twitter (News) \\\n", + "count 328 328 \n", + "mean 154 363 \n", + "std 447 984 \n", + "min 0 0 \n", + "25% 10 0 \n", + "50% 36 30 \n", + "75% 104 266 \n", + "max 5,467 11,902 \n", + "\n", + " First-order Facebook (Research) Second-order Facebook (News) \n", + "count 300 300 \n", + "mean 22 57 \n", + "std 74 127 \n", + "min 0 0 \n", + "25% 1 0 \n", + "50% 4 9 \n", + "75% 13 48 \n", + "max 906 1,214 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "display(md(\"## Summary of number of posts on Twitter and FB: by DOI\"))\n", + "by_doi_main.describe().applymap(lambda x: \"{:,.0f}\".format(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "by_url = summarize_posts(single_count(news_fb, 'news_url').groupby('news_url'), 'fb') \\\n", + " .merge(summarize_posts(single_count(news_tweets, 'news_url').groupby('news_url'), 'tw') \\\n", + " , left_on='news_url', right_on='news_url', how='outer').fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Comparing News Stories across outlets" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "### Correlations in number of posts of news stories across Twitter and FB" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " num_posts_tw num_posts_fb \\\n", + "num_posts_tw 1.00 0.72 \n", + "num_posts_fb 0.72 1.00 \n", + "num_shares_tw 0.92 0.75 \n", + "num_shares_fb 0.65 0.88 \n", + "num_likes_tw 0.93 0.74 \n", + "num_likes_fb 0.64 0.88 \n", + "\n", + " num_shares_tw num_shares_fb \\\n", + "num_posts_tw 0.92 0.65 \n", + "num_posts_fb 0.75 0.88 \n", + "num_shares_tw 1.00 0.68 \n", + "num_shares_fb 0.68 1.00 \n", + "num_likes_tw 0.98 0.68 \n", + "num_likes_fb 0.68 0.97 \n", + "\n", + " num_likes_tw num_likes_fb \n", + "num_posts_tw 0.93 0.64 \n", + "num_posts_fb 0.74 0.88 \n", + "num_shares_tw 0.98 0.68 \n", + "num_shares_fb 0.68 0.97 \n", + "num_likes_tw 1.00 0.68 \n", + "num_likes_fb 0.68 1.00 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md(\"## Comparing News Stories across outlets\"))\n", + "display(md(\"### Correlations in number of posts of news stories across Twitter and FB\"))\n", + "\n", + "display(by_url[['num_%s_%s' % (c, p) for c in ['posts', 'shares', 'likes'] for p in ['tw', 'fb']]].corr(method='spearman'))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Number of Tweets vs FB posts by News Outlet" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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AyBFydXIe26DTTmVLLgAAAAAA6HKEe8ABsgIBjf/KDTIsq935YZdfKlcg0M1dAQAAAACA/oBwDzhApsul0LixmvGTu1V85BHSf89pC00Yr8m3fEvlp58qV8Dfw10CAAAAAIC+iH2CQBewvF75h1Zo7Gc+Jef6T+4aNAzZ4XDO16UaG5WJx5Wqb5BdUCDL65VdkPs1AAAAAAAAuxHuAV1ob7ffZlMpxbdXavVPfqbImrUt44HRozT2c5+Rb3C5TNs+WG0CAAAAAIA+gm25QA9I1tZq8Ze/1irYk6To2nVa8uWvKVFd00OdAQAAAACAfEK4B3SzdKxZG/74Z2Xj8Xbns4mENvzxT0rHYt3cGQAAAAAAyDeEe0A3y6aSqn39zZw1tW+8pWwy1U0dAQAAAACAfMWZe0B3y2blZDKd1sjJdk8/wD5KNTbKSWfkOI5MlyVXKCTD5O+KAAAAAKAnEO4B3c0w5S4tVbK6usMSd0mxRFiCXiYdiymydp02/v6PLedFegYMUMWHLlDJUXNkh4I93CEAAAAA9D+kB0A3s8MhDTnvnJw1g885W64gQQl6j0wyqbqFi/T+TTe3uggmUVWltffep41/+otSkUgPdggAAAAA/RPhHtDNDNPUgOPmqnD2rHbnC2fO0MATj5dpWfv1/FRDgxK1tUpUVyvZ0KBsirP7cOCyzc1a+4v7Opzf8fQzSjc0dmNHAAAAAACJbblAj7BDIY37wmcVWbVaW//5mBJV1XKXlmjI+ecqNH6c7FBon5+ZjsUUWbNW63/zO8U2bJQkuUJBlZ95usrPOnO/nvm/nGxWmXhCMgy5fN4Dfh7yR9Pqtcp0coPztsef0MirrpRp293UFQAAAACAcA/oIXYopKLZsxQcN1ZOJiPDsvY7gMumUmpYvEQr7riz1Xi6KaLNDz6kyJp1Gvu5z8gO7+fz02mlI1HVvvmmat9eKMOyNPCE4xWeOF52OLxfz0R+SVRXdVqTrK5RNp0+4HAvFk2qOZbUpnW1sixTI8aUyHa75PMTGgIAAADA/yLcA3pYV6yoy8RiWvPzX3Y4n2psVDoalZNOKROPy/L7ZdpuuQL+Tp+dTacV27xZ733jW8pEP1i5VfvGm/IOKtOU274rT2nJAX8G9G6+wYM7rfEOGSzT7c5Zk8lmFEnGtLWxUpsatqrQG9aEAWPkdXnkdXkUaYzrH39cpE3raz94kSFNmTlYp503Rf5A7ucDfUUs2ax4JqGllSsUTyc0vnSUin2FCnsP/N8ZAAAA6FsI94A+ILZ1m9JNTe3OlZ18kkrnHq1Vd/1IkdVrdg2apoqPOEyjr7la7uLinM/ORKN67xs3twr2dotX7tDyW7+nybd8U3ZBwQF/DvRegeHD5AqHlW7s+Fy98tNPy3lWZCqT1qaGrfrBK/eqLt7QMm5btj469VwdO+Qo/fm+N7Wz8n9+LzvSe4u2yck6OuPCqfL5CfjQtzUlovrb0n/p2XWvyHGclvGKcLm+NvfTGuAvlmEYPdghAAAAehMu1ADyRCaT3bVdsbntBRmpuvp2XxMcM1oDjj1Gy2659YNgT5KyWdW+9oaWfPVGJRsa2n2tJDmOo9oFi5SJRjusia5fr2QH74/ez3EcpRoaWv6XiSfarbP8fo3/8hdldBDeDbv0I3IFAznfqyHRqG8/f3erYE+SUpmUXt74prZvq28b7O3h/Xe3K5nIdPKJgPzWnIrrkWX/0TNrX24V7EnSlsbt+ua8O9WU4GZqAAAAfICVe0Avl0pm1Nyc1MLXNmrD2hq5XJZmHjZUo8aVyh/0SJJ8g8vbfW352Wdq45//Kiedbnc+sbNKO56dpyHnnSPT1faPg2wypbqFizrtsfH99xUYMXwfPhV6g1RTk+oWLNSWfzyi5i1bZXq9Gnjcsaq46ELZRYWtVuGZtq3Q+HGa8ZO7tfEvD6j2zbekbFah8eM09COXKDRurFyBjsO9RDqpx5Y/o0Qm2e785NLxen/B9k57XrNip2Yfye819F2JTFJPr3m5w/m65gYt2v6ejh1xBKv3AAAAIIlwD+jVUsmMNq6r0YO/fVvZzAcrONavrlZhsU9XfnqOwoU+uYuL5S0vV3x763AkMGKEmlaszPkeO556RmUnnSh3YdtttYZptBv6tamz2SaZb1JNTVp7769U8+prLWPZeFyVTz2tqlfma/oPv98mNLY8HvmHVmjsZz4t5/pP7ho0zL26qCWejuvNLe90OG8ZljIZp8P53dLpbKc1QD7bULdZ6Wz7fyGz2ysb39KhQ6Yr4O783FQAAAD0fWzLBXqx5uZUm2Bvt/raZj30h4WKRRJyhYKa8LUvyfR6PygwTWVTbbfw/q9UB2f1SbtWa5WdfGLuBxiGimbO6PR90LtE1q5rFeztKRONavVP71Gqsf3fG66AX3ZBwa7/7eUNzI52XabRkY2NWzRkTLDT54wYw+Ut6NvSOb5PPqhJt9myCwAAgP6LcA/opTKZrN59a1O7wd5uWzfVq7k5JcM05RsyRLPu+YnKTjtFVsAvw7Jk+bwyOll5568YIsPseGuXf/gw+YcP63C+dO4xMn3eDufR+6SaItr68D9z1jQtX6Fssv3z9/aH27I1ceDYDucX71imoWOL5PXZHdaUDQ4rGPJ0WU9AbzSiaKgM5d5uO2PQZHltvhcAAACwC+Ee0EslE2ltXFvbaV3l1l23l5q2Lc+AUo38+BWa9Yt7dOhv7pMrFFLp3KNzvn7Ihy6QHQ53OG+Hw5p8y80KT5rUesI0NeD44zTqE1fJDna+4gq9h5NJK75jZ6d1qfr6LntPv+3TRZPP6jC0cBxHa5vW6YpPHdluwFdU4tdHrj5UgSCBBvo2r+XWzPIpHc67LVvHjTxSLpOTVQAAALAL/2UI9FKmach2t38z6Z7+t8byemXtsT13xMcuV9OKlYpva3tZQcmcIxQcPVqppojsUMcBnbuoUBO+/hWloxE1Llsh02UpPGWyLK835yUK6J0M05S7qFCJHTty1rlCe7fldm8NCBTr+kMv070L/txmS+ERFbM0c/AU+V0+ffprx2nVsp1a9X6lTNPUjEMrNGR4kfwBznZE3xf0BHT9YZfrey/fo3V1m1rNeSy3vnHsZxXkrD0AAADsgXAP6KU8XluzjhimVe93HMC4XKaGDC3M+Rx3UaGm3XG7ql5+WZVPPq1UY6N8gwer7NRT5C4u0ruf/6KKZs7UqOuukZ0jzLHDIdnhkHzl7d/Mi/xhh8MafO7ZWpnjshX/iOGyvL4ufV+/7dPhQ2dp2qBJen79a9pQt1kF3qBOG3u8irwFCnp2BcWBoEczDqnQpOnlMgzJ7eZfVehfCrwhfePYz2hrY6WeXfuKkpmUppSN1xEVM+W3/bItvicAAADwAf7rEOjFKoYXaUBZUFU7Iu3OH3ncKLn2YnWf5ffJLirUsMsulcvvU6KmVjufe16Ny5ZJkqrnv6qiQ2drwLFzZRi5z3pC31AwZYpCEyeoafmKNnOGbWvsZ/9PdkHH27X3l8/2ymd7dcHE05TMpuQyLLnaCSoM05DHw7+i0H+FPEFNGDBGo4uHK+tkZVu2TIPTVAAAANAWPzkBvZg/4Nbl1x+hR//6rtatqm4Zd9mmjjxutI6YO3KvApB0NKZ1v7xf6ab2Q0JJ2vKPR1Q0c4bsgoIu6f1ANMabFEnFtLFui3y2VyOLhsnjcsvr4ry1rmKHQ5p441e17V+Pq/LJp5WORCTDUMG0qRr1iavkKRt4UN/fNE15TX49gc7YVseXzAAAAAAS4R7Q6wVDXl142SwlEmntrGySy2WqrDws223JvZcrm5xMOmewJ0nNm7d0RbsHrDpaq7tfu19raje0jHksty6YdLpOHnOMgm7O+OsqdjisoZdcpPKzzpSTScswLRkuV87zFwEAAAAAvQvhHpAHfAG3fAG3Cov37xB1w7Qkw5D+5xKDPZmenl9F1Rhv0jef/6FqYnWtxhOZpB5Y+pgsw9Rp446T2+Jiha5i2rbchT2/WhMAAAAAsH84vAXoB0zbpYJpU3PWDDz+WJkeb86agynrZLVg25I2wd6eHl7+pOKpRDd2BQAAAABA70a4B/QDrmBQI6/+uAxX+4t1rYBfFRddKMvbc6v3osmYXlz/Rs6a5lRc2yM7u6kjAAAAAAB6P8I9oJdyHEephgY1rV6tbf9+XDvmPa9kbZ3S0dh+Pc9bPkhT77hN/mFDW40Hx43VtDvvkF1Y2AVd7z/HcZTMJDutS2ZS3dANAAAAAAD5gTP3gF7IyWQU37FDy265TfHKyg8mTFPlZ5yuoR++SHYotE/PtNxuhcaO0ZRbv6N0LKZ0Y6PsoiJZXo/scLiLP8G+89oeTRwwVuvqNnVYY8jQ4FBZN3YFAAAAAEDvRrgH9EKppoiWfPUbSjc2SpIKpk3VoNNOkXfQIMnJKrpunUITJsjaj0sw7IKw7IKwVD6oq9s+IG7LrTPGHa8nV7+grJNtt2Zm+WR5XFymAQAAAADAboR76PfSqYwSibQcZ9eFsh6PS6bhKBONqmnNWjWtXCU7HFbJkYfL8nrlCgQOaj9ONqvqV+Yr3dgow+XS+C99QelYs7b84xFF162XYVkqPuxQuUtK5Rk4QJa774RdIU9QX5xzje5+7f42Ad+wgiG67rDLFXQf3K8/AAAAAAD5xHAcx+npJvLBli1bdOKJJ2revHmqqKjo6XbQRaKRhN58eb0Wvr5RzbGUvD5bhxw5XIceNVTrv3+HmpYt+6DYMFR+5hka+uGLZYeCB62nVGOjlt/6PTWtXKUx/3e9ous3aPsTT7apM1wuTbntOwqNHSPDsg5aP90tnk4omozpubXztaJ6jbwuj04ePVdjS0Yo5Dl4X/d9lWpsUqqxQdXzX1M2lVLx7FnyDRmya1UkAAAAAADdhJV76LeiTQn9/hevqWZntGUs3pzS/OfXaNmS7br0uk9p5de/qkz0v/OOo+2PPyG7sEBDzj1b5sFaMefsOnPPXVIs7+DBWnPPve2XpdNaeefdmnH3D2QXFBycXrpIJh5XNpGQk83KME1Zfr9M22631uvyyOvy6MJJpyueSco0DPltXzd3nFuqoUEr7/qxGhYvaRnb+o9H5B00SJO/c7M8AwfIMIwe7BAAAAAA0F9wWy76pXQqo9dfWtcq2NtTbXVUby3cqdITT2wzt/WfjynT3HzQerP8PhXMmK4Bc4/RzudfzFmbrK5WfOfOg9bLgXIyGSWqa7TuV7/W21dfq7ev/ITe+czntfWRR5X673mCHXFZLgXd/t4X7DU1afXPftEq2NstXlmppTfepHRTUw90BgAAAADojwj30C8l4mktfH1jzpp3F25X0dzj2oxnolEl6+oPTmOSTNtW+emnyS4qUmIvgrv49spOa3pKorpG737+i9o57wU5qZQkKdXQqE1/fVDLbrm104CvN8rEmlX39oIO55PVNWp8f1mH8wAAAAAAdCXCPfRLjuMoEU/nrEnE0zLsDrbetnNUZTKRVqQpoXWrqrRmxU411jerOZbcr/5coZCKZs2Qu6io01pPael+vcfBlo5EtO5X9yvdFGl3PrJmrWpef0NOtv2bcXurhiVLO62penm+MvFEN3QDAAAAAOjvOHMP/ZJhGnJ7LCUTmQ5rbLclJ9M2ADS9XtnFrUO35mhSr76wVm+9sl7p9H/DKkMaN7lMZ39omgIhzz71Z3nc8paVafC5Z6vqpZc77rGgQL4hg/fp2d0lm06rbtG7OWu2Pfa4io84XO5efmbgnvbqDiLHkcRdRQAAAACAg4+Ve+iX3B6XZhw2LGfN9Jnlqn/1lTbjg844TZbX2/LP8eaUXnpmlV57Ye0HwZ4kOdKq93bogd+8rVh031fwmW63PAMHqPTYuR0UmBrz2U/LCgT2+dndIZtISp2syktUV+ddBlY4bUqnNSVHzWn1ewQAAAAAgIOFcA/9km1bOvrE0Sooav+yhnChV0ccWa7qZ59tNV569FGquOA8WZ4PVuKlUhm9/VrH5/dt21yvmp3tb03ttM9QSKOuuVojP/kJuUtLWsZDEydo2h23KTx5kkxX71yAa3rckpn7jxjPgFIpzy6VtfwBFUyf1uG8XViowmlTu7EjAAAAAEB/1jtTAaAbBIIeXfXZo/TyM6u0ZOFWpZIZuWxT0w6p0LEnj5XHTGvEFZepcdkK2QVhlZ18klyhoOxQqNVz1q+qlpPNvfxswWsbVV5RIJdt7XOfdiio8tNPVenRc+SkMzJMQ4blkh0Odf7iHmS6XCqaNVN1CxZ2WDP4nLPbfD17Ozsc0rgvfl4rbr9DTStXtZpzlxRryq3fkSsU7KHuAAAAAAD9DeEe+i3DMBQKe3XyOZN03Gnjlc04Mk1DtseS273rW6PspBM18ITjZeRYgZZM5r6YQ9q1ui/bSQCYs1fTzKtz6STJFQxq1LWf0OKVq5RuamozHxw7RiVHHpHza9tbuQsLNPGmrytRXaOdL7woJ5VS8WGHKjh6tFyhYF5+JgAAAABAfiLcQ7/ndrtawrz2dBbUVAzv/EbbEWNKZO/Hqr185ykp0Ywf36VNf31QVS+/IieVkl0QVvmZZ2jQ6af2+tWHudjhsOxwWMFRI+U4jgwjz/YXAwAAAAD6BMI94ACFCrwqLQuqekf75+q5bFOTpg+WYe5b+OM4jtKNjYrvrFLTihWy/H4VzZwh0+OVK+DvitYPOsOy5Ckt0chrrtbwKy7bdcGGYcoVDPTaswL3B8EeAAAAAKCn9J2froEeEgh69JGrD9Pv7nlVkcZEqznLZeqj1xwmr3ffvtWcTEbxHTu17JZbFa+s/GDCNFV+xuka+uGL8uqsOpfPK/m4PRYAAAAAgK5GuAd0gcJin669Ya5Wvb9DSxZsUSbraNS4AZp9xDD5/PY+X6SRaopo6dduVKqhsfVENqvtjz8hy+dVxYculOX1tP8AAAAAAADQLxDuAV3AMAwFgh7NOHSoJkwdJMeR3B6XXK59v1jByWZV/cr8tsHeHrb963GVn3Um4R4AAAAAAP0c4R7QhQzTkM/vPqBnpCMRVb8yP2dNNpFQ89atchfm1w26+cRxHMWiSTmOZEgyXYZ8vgP7tQUAAAAAoKsR7gG9jbPrzL1OZbMHv5d+qjmW1NqVVXr52dWq3hGRYUhjJg7USWdOVEGxL+ftygAAAAAAdKd93zMI4KCy/D4VzJieu8g05asY0j0N9TPNsaRefHqVHvnzOy03IDuOtHrZTt1318vatrlemUzvCVadbFaphgYl6+uVicd7uh0AAAAAQDcj3AN6GdO2VX76aTJsu8Oa0qPnyHRz3t7BEGlM6O35G9qdy2YdPfLndxRvTnVvU+1wslkl6xu07fH/6P2bv6v3bvyW1t3/GzVv26Z0rLmn2wMAAAAAdBPCPaAXcoVDmnTzTTLdbc94C02aqFGfuFqugL8HOuvbUqmM3nh5Xc6aSGNCddWxbuqoY4mqar37+S9qw29+p+j69WreulU7n3teiz71WdW+8SYBHwAAAAD0ExwcBfRCltut8Phxmn3/vap57Q01LH1Plt+vQaefKu/AgbLDoZ5usU9KpzKqr+08uKuvjaliRFE3dNS+VGOjVnz/TqXq6ttOOo5W//QezZ4ySS6/r9t7AwAAAAB0L8I9oJcy3W653W4NOv1UDTzxeBmWJdPFt+zB5LItFRR2HogVFPVsaJZuiii6NscKQ8fR9iee0vDLPiIzx/ZuAAAAAED+Y1su0MsZhiHL4yHY6wa2bemIY0flrAmEPCoeEOimjtoX3bSp05rI6tVcsAEAAAAA/QDhHgDsIVTg1czDh7Y7Z5iGzvvIDHl9PRu0ugKdh4uW3y/DsrqhGwAAAABAT2IpEADswed366SzJqpieJHmz1ujuppdZ/CNHFuik86apJIBAVk9HJoFhg+T5fMp09zxpRnlZ5wml59LVwAAAACgryPcQ7+Qbm5WNpFQ4/vLlGpoVHD0KHkHlckVDLK6CW34/G7NOHSoxk0uUzbr7NoabRny+dveXtwTTK9Xwy77iNbf/9t25wOjRio4ZnQ3dwUAAAAA6AmEe+jz0pGItj/1jDY/+Hc5qVTLuGfgAE286Ub5hgzmPDu0YZiGAkFPT7fRLsvj0cDjjpVp29r0lweUamiUJBmWpZI5R2rkNVfJDod7uEsAAAAAQHcg0UCflk2nVfXyK9r0p7+0mUvsrNLSr31Ds37+E7mLi3ugu9yyTlZNiah2RKq0onqt/LZPs8qnyONyK+Bmu2V/5woGNfCE41VyxOFK1tUpm0zJM6BUptu9V2fyAQAAAAD6BsI99GnpaFSbH/x7h/OZWEzbn3xaQy/+kEzb7sbOcstkM9oRqdbtL9+jndHqlnHDMHTK6Lm6ZMrZCnoIcPo707ZlFhTILijo6VYAAAAAAD2E23LRp6Ubm1q2LHak+pVXlY7GuqmjvRNJRvXNeXe2CvYkyXEcPb3mJT224hnF04ke6q61bCalTLRBiZ0blahcp3S0QdlkvKfbAgAAAACgX2DlHvo0J5PZi5q0JOfgN7OXstmsXt24QE3JaIc1T65+QWeNP0leV8+eCZdpjiiy9EXVzf+Hss1NkiTD5VZw2nEqPvYjsvyc+9YdIsmYmlNxvb55oaLJmCYMGKPRRcMUcPtlmVwYAwAAAAB9GeEe+jS7sFCm261sMtlhTcHUKbK83m7sKrdIKqZXNy/IWZPMpLStaYcKvKFu6qqtTKJZjQueVN3LD7Yad9JJNS16Rqm6SpWd9wUCvoOsKRHVnxY/rBfXv/7B4HKpwBPSN477rCpCg+Sy+KMeAAAAAPoqtuWiT7O8HpWdclLHBaapigsv6FXhnhxHWSfbaVkm2/mqxIPJSSVU9+o/OpyPr1+iVENVN3bU/8TTCT26/KnWwd5/NSSadPO8u3KuAAUAAAAA5D/CPfRplteroZdcrMKZM9pOmqbG3/B52cVF3d5XLj7bp+llE3PWWIapIeFB3dRR++Kb3pcy6Zw1jQueUjad6qaO+p9EOqmn17zU4XxzOq4X1r2mdDb3rxMAAAAAIH+xVwt9nh0OadwNn1diZ5W2/+dJpSMRBceM0cATj5fL7+9dq/Yk2ZZLJ48+Rv9eNU+pTPvB2Jyhs+U2e/bbN/PfM/ZyycYjUjYjqffcRNyXVEZ2KtnB75Hd3tz6jk4afbTCPbiFGwAAAABw8BDuoV+wQyHZoZBGX/dJZdMZWR63DLN3Llx1HEfavENfm32VfrDwd0pkWp8XOKlkjD487AS5ExmpB+/T8Awa3XnNkHEyXO5u6KZ/cpzOL4LZmxoAAAAAQP4i3EO/Ytq2TLt3ryJLRyLa9se/ylU2QHdffIMWVa/QishmeS2Pjh84Q8HGpDbcdKsmfP2rssM9d1mFXThQroKBSjfsbL/AtBSaemyvDVH7gvLQQLlMV85tt7MHT5PP9nVjVwAAAACA7kS4B/QyTjarTDyuxudfUt1L8zV09iyNHjpYTiKuxoW/VNX27ZKkTHNzj/Zp+kMadMmN2vbHbygb/59LGwxTZed/UabH3zPN9RMey60TRs7RM2tfbnfebdk6efQxsrktFwAAAAD6LH7iA3oZy+tVcOwYRdeuk5PJqO6tt6W32tb5Bpd3f3N7MAxTdvFgVVz7EzW986wi78+Xk83IN3yKCuecLytQKNPdu84z7Gu8tleXTD1b9fEGvbV1cas5n+3VN+Z+ViFPsIe6AwAAAAB0B8I9oJexPB4NOfcc7Xj6WamD89IKpk2V2QsuAjEsS65gkQrmnK/QrFMkSabtJdTrRiFPUNceerk+Ou18vbD+NUWTMU0eOF5Ty8bLZ/tYtQcAAAAAfRw/9QG9kF1YoNGfvl5rf35vm4DPO6hMYz//Gdmh3nP7qWnZMgOFPd1GvxXyBBTyBHTp9POVdbIyDc45BAAAAID+gnAP6IVcfr9Kj5qjgsmTtPXRx9S0cpUsr1dlp5ys4kNn9+hFGujdCPYAAAAAoH8h3AN6KZffJ5ffp5FXfVzZRFwyDLlCIRmG0dOtYT9lshlFkjEZkhxJAbdPLpM/hgEAAAAA+4+fKoEukmpqUiYaU+1bbyubSqlwxnR5SktlFxzYKjvL65Hl9XRRl+gpjfGInl//qp5c/YLqmhtU6A3rtLHH6aRRRyvs7T1brAEAAAAA+YVwD/1aOhaTk07LtG1ZPt9+PyfV2KjVP/u56t5a0DK28Y9/ln/YUE26+SZ5Sku7ol3kqcZ4k77z4k+0qWFry1h9vFEPLv2X5m98Wzcf/wUVEPABAAAAAPYDhzOhX0o1Nanhvfe16u6faNktt2r1PfcqsnatUk2R/XhWRGt+fm+rYG+32KbNWnrjt5RqbOyKtpGHUpm0nlz9Yqtgb09bGrfr8ZXPKZVJdXNnAAAAAIC+gJV76HdSTU1a9aOfqn7hopaxyJq1qpn/qspOO0XDL79UdjC418/LNDer9o23OpxP7NihppWrVXzo7APqG/kplmrWM2teylnz3Nr5Omv8SSqw7G7qCgAAAADQV7ByD/1KNp1W5dPPtAr29rTjqWfUsGTpPj2zYfGSTmuqXnxRmURyn56LvsGRo6ZkNGdNNBVT1sl2U0cAAAAAgL6EcA/9SjoS1fZ/PZ6zZsvf/6FUQ8Ou+lhMyfp6pRoa5WTbD186Gm9b4+xzv8h/hgx5XLkvRHFbtkyDP44BAAAAAPuObbnoV5xMWqmG3OffRddvkJN1FFmzVpsfelixDRvlCgZUduopKjniMNnhsDLxuDLxuCSpaPYsWT6fMs3NHT6z+IjDZXm48bY/8ro8Om7EEXo6x9bcY4YfJo/l7sauAAAAAAB9BeEe+hXD7Hx1lGHbysRiWnzDV1qNR9bcq3Q0qpLDDtWWfz6qhiXvyXRZKplzpKbdeYc2/eUB1bz+RpvnucJhFc2Y3mWfAfnF43Lrgkmn662t76quuaHNfKE3rIumnCWvTfgLAAAAANh3hHvoVwyXS+FJE9W4bHmHNQOOOUpVr8xvM15+1pmyw2Et+r/PSXtsxd3y0MPa/sSTmvTNG5WORlud2WcXhDXltu/KtQ8XdKDvKfSG9b2Tvqa/Ln1Ur21aqHQ2Lcu0NKdilj46/XwVesM93SIAAAAAIE8ZjuNwENhe2LJli0488UTNmzdPFRUVPd0ODkBk7Tot/tJXWwV0u5ler6b94HYtu+VWJWtqPxh3uzXtzju0+ItflpPJtPtcd2mppn7vVm35+0PKptMqPuxQFUyeJFcwKMOyDtrnQf5oTsWVzCSVzmbkMi25Lbd8tren2wIAAAAA5DFW7qHf8Q0ZrCnfvVmrf3KPEjurPhgfOlTjbvicKp96tlWwJ0nFhx+mmtff6DDYk6RkdbWSNTUadf21kuPIdO3dt1c2k1EmElG8coeat22Xu7hIgRHDZfn9Mm17/z4keiWf7SXMAwAAAAB0KcI99DuW16vwxIma9sPvK1lbp1RdnTwDBsgVDkmmqablbbfsessGKrZpU6fPjqxdp/DECXvdSyaRVGzDBq34/p2tAkVXMKjRn75OhTOmy+X3K5lJtaz6Mg1TtulSyBOUYRh7/V4AAAAAAKDvIdxDv2RYltwFBXIXFEgjR7SMO9msyk45Wevuu79VfSaekCsQ6PS5rtC+na2XrK3V0m98S04q1Wo8HYlo5Q/u0tTbvytj9FA9sXKenlrzkmKpXTfyDg6V6WMzLtSE0tHyu/379J4AAAAAAKDv6PzqUKAfMUxTpUfNkaesrNV43YIFKj3m6NyvdblUOG3aXr9XOhbTpr8+2CbYa+E4irkc3fPmH/TI8qdagj1J2ta0Q3e88gst3PaeUpkOXg8AAAAAAPo8wj30aulYTMn6esU2bVZ8506lGhp0sO+AsQvCmnbHrSo6ZJb0322v8codcjIZFc6a2eHrKi66QJbXs9fvk02lVPP6Gx3O+4YOVa0rpXe2v9dhzW8X/U3NqfhevycAAAAAAOhb2JaLXitZV6d1v/6tal9/s+UiC9/QCo2+7hoFRo+Wy+c7aO/tLi7WuC98XtlkQvGdVVI2K8dxNOLjV6hy4EDtfP4FZZNJSZIrFFLFxR/SwBOOk7UPPRmSnHS6w/mCY47Qv7a/lfMZ0VRMmxq2aYp3/F6/L/qPTDyuTDyuhsVLFa+qkr9iiMITJ8jy+WS63T3dHgAAAACgCxDuoVdKNTbqvW/crOatW1uNN2/eovdu+rYmf+dmFUyZLMM8eItPXcGApIAMj0fbH/+PNv/1QVl+v8rPPF3TfvA9pSIRmZYlz8CBcoVDsvY1LDEMhcaPU9OKle1PBwNqSlZ2+piGeNO+vW8vkmpqUjaRVKqhXq5gSJbXI7ugoKfb6hPS0aiqX31d6+//TUsQLUmWz6dxN3xe4SlT5PJxcy8AAAAA5Du25aLXcbJZ1bzxVptg74MCR2vvvU+ppu4JtexAQIPPOkOTvv1NhcaNUdbjU9LlU5OnRNHQIKU9QTmGte/PDYc19MMXdzif2VGtitCgTp8zOFzWaU1v42QyildWasXtP9CCqz+pxV/8ihZ+8nq9981bFFmzVplEoqdbzHtNK1Zq7c/vbRXsSVKmuVnLb/++Ejs6D44BAAAAAL0f4R56nXRTRJX/eSpnTXzbdmVisW7qSHIFAiqaOUMjvvRVva/R+sU9C/W7+xbq/h+/qp9//0W9NX+9mmPJzh/0P0Jjx3YY8BnxhM4Yf0LO15cFB6jEV7jP79vTUg0NWvylr6lx2bJW47GNG7XkqzcqvmNHD3XWN6QaGrXhj3/uuCCb1cY//VXpaLT7mgIAAAAAHBRsy0Wv4zhZpaORTusyseZOa9KZtGKpZlXH6hRPJ1QWKJGdNRR77W3FNm5WcOxoFc6cKcvnleXJfRlGLJrUPx94V+tWVrcajzen9NzjK5SIpzXn+NHyeO1O+9rNFQxo8NlnasBxx2r740+oectW2SXFGnLOWXKXlCjlcenSaefrL0v+2ea1XpdHXzrqkwp6Anv9fr1BJpHUlof/qXQHKy+ddFrr7/+txn/lS7JDwW7urm9wMmnFNmzMWVO36J2cZz4CAAAAAPID4R56HdNlyz90qBI7qzouMgzZRYU5nxNNxvT21sX68+J/qjGxK0gyZGjaoIm6Zto5anjiKW1/4j8y3W6N/+qXVDBlsixvx2eQNdQ1twn29vTqC2t16FEj5dnHY8xcwaBcwaBGXPkxZZNJGbbdcn6fLemk0Udr4oAx+vt7j2t1zXrZlq05w2br3AmnqMAbkmnk1wLcTLxZO59/MWdNw5KlBE8HYK8ulM5mD3ofAAAAAICDj3APvY4rGFDFxR9S3cJFHdYUzZ4py93xSrt0Jq23tryre9/+U6txR44WVy7TbZFq3fSVz2jVZ76ibDKp5bfdoZn3/Fj+IUPafV4mk9WC1zbk7DubcbRudZWmza7IWdcR07Zl2m1X/QXcfo0rHaXPz7lamWxGjiSfyyuPKz9vOzWkvdpSvfuG5L3hZLOSHBnmvp992BeZLkveQWWKV3a8vTk8aZJ0EC+kAQAAAAB0D36yQ6/kqxii8nPOanfOM3CARn/quv/eZtu+aKpZf178SIfz2yM7tbRhvQpnztg1kM1q84MPKd3c/lbfTCareHPnK8nizalOa/ZX0B1QgTesQm84b4M9SXIk+YcNzVljBfwyXJ3/3UMm1qj41lWqfupXqnriXkVXvqlMtEHZVEKZaIPim5crumqBkjXblIk1dNEn6P1coZAqLrkoZ82wSz8sOxTqpo4AAAAAAAcLK/fQJdLNcWUTcUmS5fN1en5dZ+xgUEMvuUglRxyuzX97SLGNm+QKBFR22skaeOxc2QUFOV9fHatVUzL3ZQEvVC7SVUcdovr/rhCsW7hI2URC8vna9uOyVDGiSMuXbM/5zMEVufvqSnvzNXcyGTnZjAyXLcMwuq23XOxwWEM+dIFW3/2TDmvKTjlZlr/tr8Oe0tF6VT54q5KV61vGIkte0ICz/09O1lHtvD8oG//g7Eb3oJEqu+BLchUOlJFnW5n3lWGaKj70UA0+/1xt++djrSdNUyOvulKBkSN6pDcAAAAAQNci3MMBycTjStbVa8tDD6v+3Xclw1DxoYdqyAXnyi4olOXZ/xVmdjCogsmT5P/KDf89f82QKxSUaXW+9TKeTuxFTVyGf+/6M0xDU2cO1rwnliubaf9As4Iin4pKD/7lFpl4XKn6em1+6GHVv/Purq/5IYdoyIXnyS4slOV2KxNrUrphpxoXPaNsslneoZMUmHiETI9fZhet+ss6WTUlokpnd61otE2Xgu6AzE62ehqGoaJZM1Vy9BzVzH+tzXxw3FhVXHBey7mD7cnEGrXzkbtaBXuSFJh0lJxMWtX/+WWb1yQr12vbH25UxTV3ywoU7sUnzG92KKihH7pA5Wecpsqnn1Wyqkq+oUM18ITjZfl8cnUSngIAAAAA8gPhHvZbJpFQ4/vLtPy2O1qdj1b55FPa8dw8Tf7OzQqNHdPuOXL7wg7u+42pg4IDOq0ZXTBU6XUfrMQrmj1LZo4Vhx6vrYuvPER//90CZbOtAz6vz9ZHP3GYfHsZFu6vTCKpxmXLtfzW77X+mj/1tHbMe16Tb/mWAiMqtPOfdym+8b2W+eiyV1U77w8adMmN8gwZL9M+sD6jyZjerVymvy39lyojuy4+KfEX6cJJp+uIobMUdOcOOe1QSKOv+6QGnXKytjz8T8W3b5ddWKjB556twmnTZIdzbxfNxqOKb1rWZjw861Tt/NdPO3xdJtqgxnefV+ER58iw+v4ff7svaxl+6UfkZDIyXK5es4ITAAAAANA1+v5PtzhoMs3NWnHHne1efOCkUlp+6/c0+96fyexkC+3BYDumppZN0NIdKzqsOWPoUdpx3x27/sE0NfTDF8nVzpbclme6LY0YU6JPf+14vf7iWq1fXS3TMjV5RrlmHTFMPr9bpnlwg5NMc0wrvveDjr/mt31PM+6+o1Ww1zKfTmr7g7dq2PU/l1lQut89NKfimrfu1TZnGtbE6vSrBX/VtqYdunDSGQq4/TmfY4dCKpw+TYFRo+Rk0jJMU3Y4vHc9bHy/zZhhe2VYljKNHd9oLEmRpS8qNOMEufrB6r3dDNOUweUZAAAAANAn8dMe9lvt2wuUTSY7nM9Eo2pc3nG4drAk6+u1+a579Mnx56msgxV8V00+X+mF7ylZWyfT7db4L39RlterdDSqdDSqTKL9z+V2u1RU4tfJZ0/UFZ+eo49dd4SOOn6MgiGvLOvgfzvVLVjYydc8psblK+SpmNBBQVoNC59UNt3xMzqTSCf1wNLHOpx/fOU8xVLtX0zSHjsUlLuwcK+DPUntrj4zLJeyqc4/VzbV+ZZtAAAAAADyBSv3sF+cbFaRlas6rWtavUYlRxzeDR3tkmpq0uof/VT17y5WqrZW3/rS/2lx/Vq9WPmO4umERhcN0zkTTpa9rVZN21/XyGuuUsHkyUrW1ilRVa2qF19SfHul7KIiDT73LHlKS9u9UdRymbLSWRmGIcvVPRm5k82qaUXnX/PYhs3yFw9UYkv7wWrzusUqOPyc/T57b9G2pcpk264c3NML617XxVPbv+24K3hHz1ThcVfIO2S8JEPJyrVqWvKMLH+BZLmkTMc3G3uHjJNp5e9twwAAAAAA7IlwD/vFME3ZRUWd1rkLCyXtuuAikU5q93qrsDf3mWr7K9McV/27iyVJ0fUbtOozX1H5rJm6+qhDZYRsudOWzLeXqXbhIlk+v+reXig7HFZ03Xpt/Z9bRateeFGlc4/WqE9+oiXgS6ezijen9N6irVq9fKcsy9C02RUaOa5U/oD7oJ5ntutrXthpnSscUjZZmfM5B9JnVay205rq5lplshlZZueXn+yrTDyuVF1EO15ervpFD8jJZlUwdYoqLrxC6WiTgpOOUmTpSx2+vvCoC2V6c28ZBgAAAAAgXxDuYb+VnXiCtvz9Hx0XmKaK5h6l6lidHnn/P3pl09tKppMaXjhEF046XZMHjlfQ07W3y0bWrm09kM2qfsFC1S9YKEmacust2vLcP+WbPkVG0C9vZrRMj6dNsLdb9cvzFRwzWuVnniFHpnZsa9Af731DqeQHK9fWrKhSuNCrK/9vjgqLDm5oVHZS51/zksNmq/LBRzosCUw6WqZ3/7/uwwuHdF5TMOSgBHvZZFKN7y/Tslu/J2WzLeP177yr+nfe1ejrP6Gioy5WqmarEtvWtHl9ySlXyVXQ+WUrAAAAAADkC87cw35zBQMaeNIJHc6PvPpK1SimG578jp5bN1+JdEKOHG2o36K7Xrtff1nyT0US0S7tyXR1fDOvXVgoDR+s5EdP10Mllfq1FuvxwipFhpVoyGeulWG1H0Zt/edjSkeiijen9Kdftg72dmusj+uBX7+taOTgnufmCgQ08KQTO5wffPaZyiYjcpLxdudNX1DBqXNlHEDwNnHAWHldHd8qbBmm5gw7ZL+fn0umuVkr77yrVbC3p3W/+p0Mw61Bl3xDgz58k3xjZskzZLzCs0/T0Ot/ruC042QdQLAJAAAAAEBvw8o97DdXMKgRV35MnoEDte2xfysT3RXUucJhVXzoAgWOP1q3zr9Hzen2g6Z5617ViaOO1pguXL0XHDNahsslJ936zDXL59PI796oHy/4g96v+uDcutU16/XChtd0wZiTdNQXP61Nd/5UkmS4XCo64jAVnHmSXAUFSnotrXynUslEx2fNVVU2qakxrkCw4+BrfzmOo1Rjo+Q4Gn75pfIMHKDt/3pc6UhE0n+/5heer7KTTpBhSeHDzlTj209KzgchmKtgoAZ95CZZvgPbEu13efWFOdfo+6/8QlmndchmyNB1h10un8t7QO/RkYb3linT3P7vJ0lyMhlVzX9NQ845S/7RM+WpGCdlsjJst0y7639dAAAAAADoaYR7OCB2KKSK889V+Wmn7gqaDMny+ZXNpFXT3KiN9Vtyvv7R5U/rU4d/TH7b1yX9mB63Bp1+qrb/+4lW4yWnn6z/7FzQKtjb0yNrntPkQz+pwKiRSlRVa+S3v6YFsQ16Yu3fVRWr1YUTzpS1urzT99+8rk6DBhd0yWfZLR2JqP7dxdr8t4cU27RZhsejkR//mGb89EfKNDfLMA25AkFZfp9Me9fKxaJjPqzCI85TbN27ysaj8laMl11UJtMXkmEc2IJd22VrYukY/fDUm/S39/6tRdvfk+M4mjpwvC6Zeo4GhQbIe5CCtOjGjZ3WxDZsUDaTkWlZsrp42zcAAAAAAL0N4R4OmOl2y3S7ZReElWpo0Mo771aipkbmFz7W6Wt3RKuUyqSljnfT7hOX36+hl1wkJ5NR5VPPtGzfLDhxrp5/86c5X/voppd1+dmnyju4XL/Z8qze2bGsZS6ZSSq0F7fiuuyu3emeaopo04N/U+Xj/2kZcxIJrfvl/Vr/699p8ne/rdD4cTL/Z0ux5fVLXr/C0zveNn0gvLZHFQXluv6wy3f9+klymaYC7oMbpnkHlHZa4ykd0ObrAQAAAABAX0W4h3ZlEgllmptVt2ChIqvXyl1SrIHHHyvL75cr0H6Ak2pq0qof/0wNS9+TXRBWkTfc6fsUesNytXP+W6qxUZlYTPEdO+UKheQpKZYVDO5VaGOHQhp+2aUaetGFql/6nrKJpJxwoMPtwbutqdmg4OHnam10W6tgT5KWVC3XFTMP0ZKFWzt8vWFII8aVdNrfvkjV17UK9vbkpNNa9cO7NeNHP5RZ0LWrBfeW3/Z1WTC7N4oOOaTdbdd7ynUOZF8USUSVcTLKOo5Mw1TIHZBpcpwqAAAAAPQXhHtoIxOPq2HJe1p5513KJpMt45v+8oDKzz5TQy+5WHYo2PZ1sZjqF70jSUo1NMrXnNbAQKl2Rqs7fK+zxp+kgPuDG2adTEbxnVVa9cO7FVnzwc237pJijb7+WoUnT5LL3/mNtK6AXwr4NfDYuZKk2lh9p69xu9xyvLb+s+qNNnMb67fINTWjQUPCqtza2O7rp8werNpkrYrUNavXMsmktj32eM6aZE2t4pU7ZB9AuJfNpGVa+fFHgenxaPhlH9WG3/+x3fnB556969e+H4inE6ps2qk/vvuw3tu5UpI0wF+s8yaeqiOGzlaILckAAAAA0C+wvANtJKprtPx7328V7O22/d9PqOqFF5VtZ+VU/eKlLf+/YPo0FRSU6roZH5bZwRlvU8rGa1TRsFZjqYZGLfny11oFe9KuEGv5bXe0Gd9bLtNSRTj3mXlHDz1Epu1WdbSm3fmfL/qtTr9sgkaPH9Bq3DQNzTh8iKYcV6Jtzdv2q7/2ZJNJxSsrO62L79ix789OxJRuqlHdqw+r+ol7VTf/H0o31igbj+1Pq93G5fep7OSTNO7LX5R38Ae/np4BAzT6U9eq4uIPdbiydG+ls2k1xJtUE6tTfXOjmrr4RueukMlmtKZmg7727B0twZ4kVcVqdf/CB/T7d/7e5TdRAwAAAAB6p/xYroNuk25u1uYH/95yVl17tjz8Tw049pi2W0EdR9KuG2uHX/YRvfeVr6vwxGP1neM/pT+vfkorqtfsmncHdOqYY3XGuOMV8nywAjCbTGrbE/9Ruqmp/Td2HK3/9W816eablInHJWfXLbGmbcvly307a8gT1BUzP6TbXvpZu/N+26ezxhwvM5lSsb9QVbHaNjU7otW6/c0f68PHna/TLzxOq9dtl2kaGjysQO/sWKrbXv+TfnT6t1q9JtXUJCedkZPJyLBdcvn9LZdedMa0bblL9+KMuQEDOq3ZUyYeVdM7z6r2+T9LclrG6156UEXHfUTh2afJ8vbeVV+uYEClc45U4dQpu0Jmx9n1eyAUknGA21GbElE9t/YV/WfV82pI7Pp9OHHAWF058yKVhwbK6+odN+5GkjH97M3ftbmteLdXNr6ls8afqCCr9wAAAACgzyPcQyvZREL1776bsyZVX69MPCH7f7K9wulTJUlDL7lIa+75pVINjap65N8KLl2uT5x3hrzHXqSsk5WRTCtcWCKfp/XW3nQspqrnX8z53rGNm5SJNev9m2+RYblUcuThGnT6qXIyIdnBtluFdzMMQ6O8Zfr8zMv0u2WPtQQ3kjS0YLA+O/2j8kSS2nDfr3X6R0/Uyup17T6nJlanpzbOk89n69H6p5R1stqybrtS2bROG3u8PP8NfzLxuJq3btP6X/9WjcuWS5JcoaDKzzxD5WeeITscyvk5JcnyeDTkvLNV9ULHXxO7sFC+wZ3f4runxJaVqn3+T+3MOKp78a9yDximwLhD9+mZ3c0wzQPaityepkRE97z5B72z/b1W48urVuvGZ+/QN4/7vCaUju4V59lVRWtU19yQs+ZfK57TtYde2msCSQAAAADAwUG4h3YY+1Vi+QMqOWqOrEBAsY0bW8Yjq9cocmfrm2oHn3eOhl/20f9ZxWbsWpHXiUxzTKmGRjnptLY99m/tnPeCJn/3Zhkul1ze9lfwpZqatPHue1RUXKhbz/ukarIRNSQiKg8OkKu2STV33y/r8MNkBwMabRZrculYvV+9us1zPJZbH595sf7w7j+0tnbXZ3Rbts6dcIrOnXCK/LZPTiajyLr1eu8b32q1AjLdFNHmB/+uxmXLNf7LN+xVwOcuKdHAE0/QznnPt500TY39/Gdl7cM21EysUbUvPZCzpu7lB+WtGC/L3/mFKH3JpoZtbYK93TJOVj9/6w+6/aSvqsDb+a/bwVYTq+u0pra5TqlMmnAPAAAAAPo4wj20Ynk8Kpo9U1Uvvtxhjbu4SJanbWBgh0Maff21qnm97YUU/yuyZq2yiUSrcM+wTAVHj1LD0g8CFsvnVdHRc+QaUi5FY2patFiGy251W2o6EtGqu36syd/9dofhnpPOqGHJUslxVPvCy/KUlcnn92lnbZ1kGio++hhlA2GVX3ih1v70Z/rUFz6ll0uX6an1r6gh0STTMDV78DRdPv18Bd0B3TDnGm1u2CbLsDS0YLA8Lo+89q6vSSoS0Zqf/KzDrc0NS5YqsnatimbO6PTrZIdCGvHxKxSaMF5bHnpYiZ07JUkFU6doxJUfk3fIEJmuvf82drJZJSvbX5W4W3LHBjk5tmX3RdFks/694tmcNVXRGjXEG7ss3MumUsqmUjJctiz3vl05PCDQ+a3MA/wlcufJRSkAAAAAgP3HT35oxfL5NPSSi1X9yqtyMpl2ayou+lCHq8XsUFDegZ2fAecKBORkWgdIdiikoR++uCXcG/SxDyt8wly9sOUtra3bpGC5T6ec/EklDY/swkKl6utbXtu8ZauStXXyFBe3+37ZZKLlTEBJSuzYoaRta9h118s1YqwWvlOlhi0JFcYaNev/blBy3TpNXblNJ1z2dTmGZJqmbMuWV4YyjdVK1dRqRLBEjuPI2bhaVvlwZQOGTJdb6YZGxStzX3Kx7dF/KTR2jFw5thJ/8HUJquzE41V8+KFSJiuZhgzLkh3aj5BpLxZl9keZbFp18fZvQd5TfbxRwzTkgN4rHY0qHYlo+xNPqnnLVrmLi1V+9plyFxft9a9pqb9Ipf5iVbdzNuRuZ084uWWbOAAAAACg7yLcQxvu4iJN/OaNWvn9O5Vp3mObrGlqyPnnasDco3OuFguOHaOSI49Q08qVSta2v31wwNxjtPOllzXwhONanZUXGDFcQz50gYwBRdo8vkQ3PnuLnD1CuVc2vq3JA8bps3ffplWf/arSkUhLb+loTLFksxw58rm8rc5GMz0eyTQ/WE1nGBr91a/pnY3SW/cuatXbgtc26ZDDh+iQQ49UICW5i4okSZlEs2LVO1UfMfXyS3FtXLdElmVq/KQBOnpuRoFkjbzFA5RqyH0WmiQl6+o7DE/bY1iW3F1wxpxhWvIOnaj45uUd1niGjJdhWgf8XvnEtmwNDJRofd2mnHUl/qIDep90JKrtTz6lTX/+a6vxHc8+pwHHzdXIT1wtO9R54Btw+/W5I6/SLS/8WOls25urTx49VyW+wgPqFQAAAACQHwj30Ibl9apg8iTNuu8Xu7aQrlknd0mxSo86UpbXJ1fA3+7rUk1NykSjqlv0jkITxmvwOWcpWV+vDb//kxI7PljJFhw3VnZBWCvvvEsFUybJDgbVlIgqnU0rmomp4MIzlXYyeuyVX7YK9nZ7v2qVHlo3T2dcc4U2/ujnCk+dovJPXaV3Y5v12uu/luM4OrRiuo6omKmgOyDLtGTatooPO1S1b7wpSSqcNVOViYDeen1lu59lwZtbNXjIBBXtsW04mUhrxepmPfnvNS1j6VRWSxZt1/uLd+jSq6ZqiD8ud2nnWya95WUy9vLW3FyaEhHFUs16d/syZZXVtLKJCnuCrW4h3pPlC6n4hMu07Q/f6PCZxSdcJsvf8+fKdSef7dV5E0/Vm1ve6bCmIlyukPvAbp+NrFvXJtjbrerFlxUYNUrlZ57e6VZry7Q0snCo7jz1G/rrkse0cNsSZZ2sKsLlunDS6Zo+aBI35QIAAABAP0G4h3aZbrfMZEqFM6YrPGGCDNuWYRgdB3sNjVr1o5+o/p13W42HJozXhK99WSt/cJcSVVUqPfooDT73LC2/7fuSpJp3F6upNKD7FvxFy6o+uMBi4oCxuuaQj+jR5U9rwbYlbd7vpQ1v6KLTvq3gxAkq/NTH9PW3ft7qBtwlO5brwSX/0i0nfFFDwoPkCgQ06pqrFVm1SsnaOpWccY4eeX5rzq/Bq/M3a+y0obL137Pqklk9+fiadmszmaz+8cByXfeFI+XxBxQYOVLR9es7fHbFhRfI5W//a7m3GuNNuufN3+vdymWtxseXjtINcz6pQl/7K/3cA4ZpwLmfU/UT98pJJ1vGDZdbpadfK/fAEQfUV74qC5TqxFFHad66V9vMeVweff7IqzsMTfdGqrFJmx/4W86abY8+poHHzZW5F6s03S63hoQH6dOHfUypbFqSI8lQ2BOUYbD/GgAAAAD6C8I9tOE4jpI1tVp77y9Vt/CdlrPq/MOHa/Snr1Ng+DBZe1xckWps0sof3r3rwor/0bRipdb87Oea/O1vKhOPq/att/X+zd9VurFR7tISuY+ara8/933FUs2tXre8arVue/ln+tZxn9eG+i1tzhZLZdOqSzRp0LVX6juLftcq2Nstmorplhd+pB+dfrPC3pDcxUWafted2vroY3IPGKDqHRvbvGZPNTujLSsHs+m03nl76678pAOxSFI7tzVp1KTBGnfD57XkK19XJhZrU1d28knylg9qM55sjiuVkrZsqlfltkaFwl6NnTRQbrclj7f1Kr+mREQ/ef23WrpzRZvnrKxep9tfvkc3Hfc5hdsJo0yPX4Fxh8s/crpiG5YoXbtdrqJB8o+cJsP2ynS3fylJXxf0BHTp9PM1tWyiHln2pDY1bJVtujRn2CG6eMpZKvIWHFho5mTVuKL9laK7JWvrlE213Wabi9/t2/+eAAAAAAB5j3APbaQaGrXkq19Xsrqm1Xhs40a99/WbNPX7tys0dswH9Y2N7QZ7u0XXrVfz1q1a//s/qnnT5pbxkjNP1UNrn20T7O3WnIrr3yue00mjj9aDS/+lMcUjVB4aqFQmrfd3rpTP5VGV0aCqaE27r5ekpmRUy3eu1iHlU2XZttzFRRp22UfVHM/sulwiR1gnSfpvmJPJGqra2X6fe9pZFdMoSd7yQZr50x9p80MPq+qll5VNJBQYMVwVF39IBVOntjpXLdUUUSISU1VdWg/9danizamWOdM0NPeUsTr0qBHy+d0ffK5EpN1gb7cN9Vu0I1LVbrgnSabbI7k9Ck0+ptPP1J8E3QHNGTZbUwaO++86OMlt2fLaBx54OpJMl0vZZDJnnWGy6g4AAAAAsPcI99BKNp3WjmeebRPs7eZkMlr3y19p0s03yQ6HJUk1r7/R6XNr31qg8IQJrcK94GGz9Mbrd+d83Ztb39FFU87UtLKJ2t60Q+vqNsnr8ujciafI7wnojej2Tt/7nR3LNNUul7ekRKbbLcvtlpVOatTYUq1bVd3h60aOLZFl7QpaUkZa/mD7Z+QNGVaoQ+YM18DysHx+W7FoUv6AW54BpRp51RUafumHJe0Kd/73UoxUU0RbH/uX/EefoL/+/l1l/ucG4WzW0YtPrVIo5NX0wypaLgl5c+viTj/3ixve0Oji4TINs9NatBb2dv2Zg6bbreIjDlP1y/M7rAmMHCnD4o9lAAAAAMDe46dItJKORLTj2edy1kTWrJWTzshxHMWiSYXnnqiJh81RbN061Tz+mCJr1rZ5jZPJyLBa38Bq2Ha7N33uaXb5VCUzSX3/lV+02nr78LInddSwQ3TxlLP1zxVPKZ5OdPgMj+mSaVlKNzfL7d61+s3nd+uksybq/h/Pl5Ntu3zPMKSTzpokn9+tpkREP3/zj7r40PO18LXWt6mefPZElQwI6rUX1mrT+l1bhwcPLdDxp43XkOFF8vq8rbYw/6/GZcvkuDx6/bVtbYK9Pb34zCqNm1KmQNAjScpkOt+6mclm1LL8DD3O5fNp2Ec/rJrX3pCTbv/Xb8THPya7INzNnQEAAAAA8hlLetBGOhLNOW+4XEqZbi1bvF1//MXr+skP5uueny7UvGWGCq/6jAZfdnmb1xRMm9pywYTp9ar8rDNkh4Iq9HYcZNiWrYunnK1vv/Cjds/Ue3XTAj256gWdPf7knP0eXTpNG//4Z6UbGuVkPwjQiksD+ujVh8ofcLeq9wVsffjqw1QyYNdtoxvrt2rR9qXantyuSTM/OCvvsGNGymVbevC3b7cEe5K0bXOD/nL/W3rnrc1KxFPqSKqhUZv/9pD8k6Zo9YqOVxBKUlNDXKlkpuWfZ5RPzlkvSYcNmdGy0g+9g7ukRFNuvUXu4qJW41bAr3Ff/LyCe2x3BwAAAABgb7ByD60Ypin/sKFqynHwf8Unr9Ub8zfp1Rdar9DbuK5Wf1hfq4suO0RDr7S0+fe/l0xT4YkTVDhjmsJTJslJpXaFg41NMmNpnTHmOP31vX+1+z5zhs7WW1vfUXMq3mEv89bN152n3qRHlv1HGaftyrcJpaMVaIhr/Suvquzkk+UpGyjLs2v1m9vj0vAxpbr+K8dqZ2WT6mtiKiz2a2B5SB6vLZfLVDwV11OrX5Qk/XLxH3XjCZ9VySC/3n11q2YeNlS/+UnHWyyf+/cyTZkxuM1lGB9wFF23XoZptFzckUt2j5qBgRJVhMu1pbH9bcnFvkKNKR7e6TPRvSy3W8GxYzTjx3epedt2NW/dKndJiYKjRsry+WS63Z0/BAAAAACAPbCsB63Y4bAqLrqww3lXKCT/5Oltgr0WjvSvh95T0dxjNeV739WMH/1QQy/+kJpWrlK2Oa7GZSu0+sc/05qf/Vzvf/VGHVM0WZMGjG33UbMHT9XCbR1f1CH999bceL1OH3t8q5tMDRk6pHyqPjPhQ9r643slSVUvvtRmC67LZSoQ9GjkmFLNPHyYRo4tVSDokcu161sj42RaLvxIpBP67qs/0srQIl3yf9O1fWuD0umOt9I6jrRk4Zac/Ztut2KrV2nk6OKcdf6AW3GnWU2JXasqw96Qbjz2MyoLlLapLfIW6NvHf0GhDi7TQM8yXS7ZBQUKT5ygspNOVNHMGbILCgj2AAAAAAD7hZV7aCM0YbwGnnSCdj73fJu5sjPP0DuLduR8fSKe1ratTSqo3Kr1v/6dsomEZBgadNopKj/jdLmLi5SoqpK3vFyZ7dX6v/EXauXIHfr36nmqidWpxF+k0yuO1OhA+V52bOiskkN0WvlhWl27QVknq7HFIxRftlrrv/FdperqJUnpWEzGPp4/57E8GlM8Qu/t3LWSMZ1N6/kNryqRTWhizeGdvr56Z0TZbLbd7bGG26PSY45S9ZP/0Zwv36Tl7+1QRwv4Zh1doSfWPyu/26uLJp8pt8utUn+Rbjv5q1pXu1GvbHxLWSerI4bO1sTS0Qp6Aq3CTmBPjuOoKRFRQ6JJ25t2KuwJqjxUJr/tk82FHgAAAACQV/gpDm3YwaBGXHmFBhw7V1v+/g81b90mVzik8jPPUOGRR+qth5d3+oy66qgyb7y1K9iTNObT1yvTHNOSr92oTDTWUhcYOUKjP3WdJkXcmjjrSsXr6pSqqlbk7y+pduByzTxivNbWbuzwfVymS0PCg2TV79DKL92s4NAKyTBUW1CggScer9Jv3SQ5jrKplNLR6D6vjnJZLp0yZq4eW/lMq62zjYmIgsUdbbf9QMmAYIfn3rl8Xg295GJVz/+8Eove0HmXzNZjD72nbKZ1wjf10HKVT/bq16+9Jpdp6azxJ8nt2vU5wp6gZpRP1uSB4yTtOqewp6QyKWUdR27LJljsxTLZjCojVbrr1V+12tYddAd01axLNKt8ivxuXw92CAAAAADYF4R7aJcdCqpw2lQFRo6Uk0nLMAy5QiE5zq6LKDoTDphKNTRIkgYce4ycTFrrf/P7NnXR9Ru0/Nbvacpt35GRklZ983ZlmndtgzU9Hh13wR369/qXlOjgNtzjRhwhj+WWVTZQ7qJCRdas1bDLPir/0KHa/Le/K7pu1yUernBY5WedodC4sbJDoU77b0pElP3vGX4Bl0efnvUR/XzhA3K0K3hbtnOVPn78R2S5TGU62JprGNK0Q4bkfB93cZGm/eB2rbzzbg2fe6Su/soRWrW0SnWVcfmCLk2YPUAbIht1++s/UTqbVjqbVl2sXgXe1p+hJ0O9xkREG+u26IUNrymTzWjW4KmaOWiyAm6/LNPq/AHoVo2JJt303J2KpmKtxiPJqH76xm/1tWM+pRnlk2UanNoAAAAAAPmAcA852aHW57YZkmYfOVyvdXTmniSP16VBA31avmZXTdmpp2jVXT/qsD7V0KCql1/RoNNOkWfgAMU2bpIkZRMJ1TzwsG668BrdseC3bcKIQwdP00emnSs7kVHj2vUa+4XPaefzL8hTUqwVd/xAe+5xTTc2avNfH1R07TqN+cynOgz4Iomollev0aPLn9bWxkqFPEGdMupoHT1kpn560tf08Op5WlGzXm7LrdrmKp1xwWT9++/tnwt4/BkT5Hbn/hYzXS75hw3T1Nu+o4iR0p2v/lxDwoNUOr5Y21MxPfTWe2r835uC9+Lyje5S19yg77z4Y21trGwZe33zIgXcft1y/Bd3raok4Os1kpmUnlj1fJvvpT394d2HNbp4RJsAGQAAAADQOxHuYZ95fS4dc9IYvfLcmraThnTuxVO089+7bsC1fF4ZhqFkTW3OZ9a89rrKTj5RZaecrPX3/6ZlvPbF+SpIJPWDyz6jFU2btCKyRUG3X8eOPFIhT1C+jKnKp5/Uxj/+RUWzZ2n0p6/Xu5+/ocMArPbNtxS/6IJ2w71IIqr7Fz6g1zcvbBmLpZr1pyX/1DPr5uvmI67Rh4wSZcdMkDJpOe/Ok10+WZdeOVUvPr9ZWzfVS5LKBod13GnjNPz/27vv+Cqrw4/jn7uz9wICgYCEPUKCbBGQIYKTUZHhqMU96gBrrVVb60RRW7WO+hNcoHUrskSmyAbZJBASEggJ2cndvz9SLlyzEBGIfN+vF68XOec8z3OeG4+BL2ckRxEQ2PCMOoPBgCU8nICyUrrFtuPzPYvrbBtkCSQiIKzBe54OpfYynl7+il+wd1S5o4K/LHqW5y5+WCHRWaTSWcmyfT/U2ya39CBOj/M09UhERERERER+KYV78rMFBlnpdUFrEpqGsWT+Lg7lVs8sa9kmmoGDWhIVZWPjwgUA2Jo0xWBpOODyOBx43W5C255HaPt2lG7b7qsrXrma4lU/ENMznYnXT8EYGIg1rDrgchQX4wwNpM3z/8BgMlFeWYo5KAhXSUmdzzrw8We0ue1mTAEBfuXbDu/2C/aOd7Asn9k7vmEckVTMfdpXXrl5CYHN2zN2/G0YAsMBMBgNBIfYGnznn7IYTQxv1Y9v9i7D4a49XBnZagA279kxE67EXsaugsw668udFazP3cIFLXtpD76zhBewux0NtnN53L9+Z0REREREROSU0KZK8rM4i0twFhdjclZw3nkRjBuXwi23pXLrbakMSnFR+fkHmI1eml5+KcGtW3HebTdVz94z158jh7Q9DwxGsma/R6vrr6X5+LFYoyIBMFqtxA0eRPPxY9n9z1fAU73HXaWzihxHIR8GZnH3DzO56/vneGPvV0T9+Q7ixl9Z57McRUV4nC6/shJ7Gf/d+nW9fVyVsx5b+141yqv2b8PiLiMkLICQsICTCvYAzEFBBLlNPJh+IyFW/30NDRgYmtSHC2O6YjE2nMlXVTqprHDUuR/gqbAtf1eDbdbkbDqhMElOD7PRzHlRreptE2C2EWgOqLeNiIiIiIiInD00c09OiLOsjJItP7L/g7mUZ2RiCrARM6A/zcePpepALgc++4LwCwYRfukYNmw+gjepD8mDL6GqvAjn9s3E9O9L/uIldd6/+dgxBMTHETd4IJunP0hMvz6cd9cdmIOD8brdFK7+ga1/fYzQdikYLRYqq+xUVblwl9u4ouVoLj9vJPP3fcvifStYc2Azd6dNIvrIYArmLazxrMBmTTHa/E/N9Xg85JTWXF56PLfHTZUBMJrBcywcNIfHYomI+XkfaB0CgkOJydrPP9JuJrM8j93lOYSaAukZ3xlnRhbhYVGYg+o+ybSi3MG+3QWsX52F0+khqXU03c9PxGNyUeouJSwgFKvJQpDll5+Gaj6BkNFkNGFAs/bOFiHWIMZ0GsmGvB/rbDMouS+B5pMLqEVEREREROT0U7gnDXKWlpE16x3yvp7nK3PbHRR+/wNF6zbQ6fFHaX7L7fz3nfVk7lrnd22zFhFcfkUqYZ06UZmdQ9mumvv0JU/9PbaYGAwGAxHduxHWsQP5i5fUCAOtUZEk33g9Diws+2YXa1dm4XRULx8MDrVx/oXp9OjZlWdXv8wLm97l2cvuomD+Yt9Mv6OaXjoKk9U/3MPjJswaQqWzqt7PwmYJoPK4YM9gthJ3xT0YA0Lquaqa1+vFU1GKx1mJp6ocU3AEBrMVU+Cxa83BQUR06ICzuIRm2/NpUukBQwVWTyHRnbtiCat7v73S4ire+udKCg+X+8r27Slg2cJdjJ7YibVVq5mX+S3pzbpyXeo4Iv+3jPhkdY5vhwGD7wTh2lzYqjc2s7XOejn9mobGc3WXy3hn08c16jrEteXKDiOw6nsmIiIiIiLSaCjckwZV5eX5gj1zWBjNLh1FZFoP7IcLMFotOA1WPpq1nn17Cmpcm5NVxJwPdnDlyERaXjsZR2Ehh79biqusnKAWLYgfOgRLVBTm4CAALKGhpPzxLgpWrSLno0+oys3FHBJC/NAhNB09Cpc1iE/f28jOrQf9nlNeamfRp7voc1Errmh7CXN2fMrWI5lEd+xA8eYtvnaJY67EEh5Ro5+BXguDEnvy7o4v6/wczotuhdHpAqMFgwGC2vcmasA4TKFRGBo4EdbjqMJxaC/5X7yM8/D+/5UaCGzVhdiRN2EKi/HtS2cOCsIcFESTEcPx2B0YTEbMwcF135zqGXsfzlrnF+z5nu328unbW5jyx4EsylrO99nr2VuUzd8G30vYLzjsIsBso0+LHizPWlNrfZOQOJKjkk76/vLrCLYGcVHr/pyf2J0vdiwkq/gAobZgRrYdTPPwJoTaGg6qRURERERE5OyhcE/q5HZ7qCy34w6OoP2M5yndtJGYbp3I+ehjst59H6/LhTU6isQHHqk12Dvq4IESypwmCl95BVNgIDED+hOY3AZTdAxOtxeMNsxuDyZT9RaQlrBQ4ocMJur8nhioPgTAFBiIyWrlyIGSGsHe8b5ftI8p96bz0a4vyHYU0DQ+Dn40Etr2PJqPG0No27aYQ2oGZQaXiwFNU1mUvZqD5Ydr1JuNZq5NGU2wMYDQ21+pvsZsxWg7seWtjsPZHPi/P4P3+FmEXiozN5Lzn+k0u/5pzCERfteYbDZMthNbHmmvcpGVUfeJxB63l61rD9IvMZ2Fe5dxsCyfldnruCi5P0bjyW29GWwN4rrUcXi9Xlb85CCS5MgW3Nf/JsIUFJ2Vgq1BBFuDmNTtKuxuB2ajiUCL9tkTERERERFpjBTuSa0qyh2sW5XF90szKS+1YzYb6ZzajL6WYOzFJXhdLsyhISRN/T079pY2eL8fdxST0qE9lXkHCeiaxtLlOfy4YQ8ul4fgUBs9+7Wke8/mGE0GgoJtGIxGrOHHlo2WOSrwVjr5Yfneep/jdnvYv+cIbaNbERMcTfPfXUDCsKFUZmdTdfAg4V0613qdyWbGsDuHP/e4gdl7vmH1gQ24/xfEnRfdimtTRmHbtg96N8UU/POWs7orSij4+tWfBHvH1ZcdoWTdPCL6XoHR1PDJwrXJO1DccJu9pbRISvR9PX/3UnolphL+C2bvhdpCuKHH77im2xVszNuGy+2kQ1xbwm2hv2hWoJweVrMFq/nk/psTERERERGRs4PCPfFT5bLjqvLywZtryd57xFfucnlYvzqb7T8eZMq1vydk3XIC+qWztTwHV4ETgKTkKNL6tiQqpnpmXHFRJWtX7GPPjnw8Hi+WiAjCL76MN1/bSEX5sRNUy0vtLP5qBzt/PMjIqzpTXmonIsKGJaB61lpRZTH/+mEWyWFJGMoTGn6HSjdB5kBSI9qw/qbb8DiqnxXVqyexAwdirOXkXq/HhS0ikIKPvuDSxKZMHDCNSpcdm8VGVeY+PMs2EjZ6GJuL9rJh1zZsJisDWp5PREBYg8sYvW439tw99bYp3bCQsB7DMAZHNPh+tbFY6l8WDGC2GHF6j+0XWO6sqHe/vBMVYgsmhGAGJ/f9xfcSERERERERkZ9H4Z74VDqryCjMomgXfsGeX5tyJ/MX7GPIFRfwxd5FhFqD6ZbSkvCwYIJDrKxYvIcD+6tnkcU1CaXXBcl0SUvE4rFjqWrFgvn7/IK94+VkFbF3dwHZWUcYPCSZ4MBKqgLMPLx4BgdKDxJsCaRTs2TYXP97xCQE0c3cEWfuIV+wB2CLjsFoqf0/eYPZQtnWxTQdOpDy7Hxynn6RqryDmIKCaD5hLI7h/blnwT8orirxXfPJ9m/oHN+OO3pfT5gtBLvLTpXLQU5JLi6Ph8TwBGwmK1aPu/4OAx57+S86VbZJs3DMZiMuV+2zAwHapsbwRf4K39eJYU2wnMCJtyIiIiIiIiJy9tLf7MXnQOlBysvtbFpZ995tAHt25HOxsTPRu1MwW4zEDY6gIDuXuf/nf1LuodxSPn1vIwMuOo8efVqAK47dH39b773Xf5/FBcNSWPZtJmnNHexJNHKgtHqPvdU5G7jygtGs+CYTj6f2GWeh4QHExYdRWhaCJTQGg9mM11U9W63JyBG1ztoDMNmCiex1Gdn/vpuApI60nHAx5tBoPPYqXImJTF/0NGWOmodVbD64nf+s+4DJ3cfw1c5FfL5zIQ539UxGg8FAz2bduCF1HObIJriO5Nb53tbYFmA4+XDPYjXRe2AySxfUPI0YICommJjEQH78dqev7MoOIwi2Bp30M0VERERERETkzDu5nfTlN6fMUcG7mz4h3BZK8ZHKett6vVBeYmfTmmzWrczCXuliwRfb62y/dMEuvF5weY14G1gFWlxUSXCIlc0b8whKSWF+xjJfncPtZHHWUoaPbVdrDma1mRh7XQ+W5KzguZWvs/HwTsI7dwIgfvhQzGFh9T7bHB5L7KV3ULV/GwVfzuTg+3+hbNsiVudsrDXYOyopohkfb/uaj7Z97Qv2ALxeL99nr+ep5a8QOuaeep8d0e8qTEH1968+VpuZXhck039IG8xm/2GdlBzFZdd34sX1b/jKLm47iMTwpif9PBERERERERE5O2jmngDg9rjZcmgHw1qUERYRwOGDZXU3NoDFZsbj8ZLcNoaMnYdx17Mc1OuFTWuy6ZbeHN/xt3UICw+kotyBy+nBYDRR6fQPGj/d/Q0B7QK45s401i3JYX/GEYxGI207x9K5VwIf7PyIlTlrADhoP0Jys2ZEnd+TmP59sYTUvzee0RZI8HlpBN72CmVbvsOel4Gly0DW7F9a5zUWk4WuTToy7ZvH62yzsyCTfNyEpZxPxY7va9SHdh1MQLOUevt2IgKDrPQd1Iae/VqRm1OMy+kmrmkYpe4S3t7+AWWOcromtOfKDheTGNaEkBOYtVfmqMDtqZ75GGwJwmzS/zJEREREREREzib6m7r4WXJgOb16DWTRJ7tq1CU0CyO1V4vq/d0sRqbedwFmo4FDB8sICLRQVems5Y7VCg9XkF9VQOu2sezZkV9nu249m7N5XQ5xCaG4jdA6Kom9Rdl+bT7Y/ikLA5dyUecLGNCnBS0imvH13kX8admbVBwXBjaNaErzcZ0xBQdjNJkotZfh9rhxed1YjBYCzDZsZqvfvY22QIy2QMLPH43X7aLS7YT9y6hLm6iWbMvfhaeOk3CP+mbPUn5/yc0Et02neM1XeMpLsEQ3IaLvlVjjWmIKrD94PFFWmxmrzUybdnG+sghPIDf3nAR4MRnNJxTqlTsqyCnJY86PX5BxJIsAs42BLXtxUZsBhFlDMBo16VdERERERETkbKBwTwAwG010jW/PmgObuPKCS2jaPIID+4sAMJoMXDq+GxaLiR+W72XRlzuw2kx06NqUbumJlBZVMunm3nz3zU62b86r9f4JLUIosBcy5LIUcmYW1RoENkkMJ6l1NIu+3M6wMe34v+1zGHHehSzMWF6jbUHlEd7b9jEd41Lon5TOpzvn+dVbjGa6RDTnwP/dT9x1/+Cgo5Q31r7P9sPVe9LZzDYubNWbKztcTHhAaI37GwwGDGYLgSYTvZunsjFva63vZTWZsbtqPyDkeJWuKrwmCyGdBxLUJhWvx4vBZD5loV59jEZjre9Yl3JHBV/tWswHWz73lZXay5jz4xfM2/0dfxtyH/EhMb9GV0VERERERETkZ9L0GwEg2BrE+C6j8Xq9PL36nwy9ug29BrckMNjCxVd2Jnd/ER/8Zw2Zuw5TVemkpKiKVUsy+L9/raJpi0i++mgzvQa0okWrKL/7hkcGMnFqL2Jjw9m3xMm2dXlcd3sfOqc2w2Sq/s8vMNhCnwtbc8mYLqxbtY+JN51Pm7bxXNi0P6VVZUzudlWtfY4LjmFStyv5eNu8GnXXdb4Mx/pFWMJiyCk/zPT5//AFewB2l52vd33L35bMpMRe9xJko8FIj6ZdCLXVHsIdKD3EedGtGvx8O8a1xWq2YDAYMAWFYw6JOC3B3skorCzyC/aOV2Iv5fmVr9f7mYmIiIiIiIjI6aOZe+KTEBLHnb2v58XVb/Gnpf9gQPPzGXPLBdhcQXz+waZar6kod/D1f7dw/oBWfPbBJoZd1pF3/r0agOBQG2OvTePLuZvJySryXbPhh2x69G7BLdMHYgAqyp1k7jqMyQRNW0Tw5dwtHCmoIDjURudeTeiVlk77oefx1c7FviWiQ5L7kdq0E7lFB/xOfE2JSWbceUOIPZRL+fefETLufl5Z9y5uj7vW/u8tymZD7o/0T+qJoY7TakOtwTw66B4e+fY5CiuL/OoSw5rQNDSepqHxvlN9f8pistC7eSputxuj+ezO06ucVXyy7Zt62+wu3Euls5KwOgJPERERERERETl9FO6JT6AlgNSmnfnnyMdYl7OZrNIDVHgqWbe49qW2R+VkFREZFURZqR2LxURYRAAlRVVcNKo9yxfu9gv2AEqKqlj81U6WzNvFTXf2wlxeROfUJnw0eyP79hT42hUfqWTZVxlsW3uQcTem0jy8CZ3iU+gY15aowAiMBiPG/A3c2WIA5m7VJ7+68vfjWjSH8pwdGMxWPOGx7CvKqbf/X+5cRLeEjoQF1B5WGY1GEkJjeXLoA+wrymFD3lYCzDb6JaUTYg0mxBrE9AG38ODCpymuKvG71mw0c3uva5mz5Qv6tkjnvOhWWM2WevtzJjncTvaXHGiw3cGyAuJDYk9Dj0RERERERESkPgr3xI/NbMVU5aBfQlccse2p8phYdqj+cAzgSGEFIaE2igormHDj+RhNBsxmI5+8t7HOazweL2vW5tEszUzxxmy/YO94BYfKWbVwL72HpvP86tdoH9MGo6F6BpytaWvyX7691usM1gDKHeUN9r24qhQP9R+IYTQYCQsIpXNCOzontKtRHxsczVPD/sSyvav5PmcDLo+L9rFtGJB0PvP3LGNBxjKW7F3Fi5c8htUc3mCfzhSj0UiINbjBdidyKIeIiIiIiIiI/PrO7jWCclp53W6qDh5i+5PPkP/td2Q++yJmLwQGWRu8NjDIit3uwmwxMfettRw8WEJ+filej7fe6/btLqBJQBPWLcuut93mtQeosju4Me13BFoCKbWXUWYvxxQcSUDLzrVe46kqJzwwssG+J4TGYTaYGmxXH6PBiMPtZGdBJn1bpDGoVR8cbid/WfQs8/d8B4DT4+LbzJW4PK5f9KxfU4g1mBHnXVhvm8iAcKKDGv5cRUREREREROTXp5l74uMsLmbjPffjKimhyYhhGIwGCIDOvRLI3HW4zutCwwOwWIy4nB6at4qk98DWJCZFkJdb1OAzTSYjRoOJIwUV9ffN4cZqsOIwuPl0+zdszd+FxWjmwlZ96H7F3ViXfEDJ2q/8rrE1aY01IJjO8e3YfHB7nfe+vP0wQmwNz1ZrSOaRLFZlr2NV9ro622w6uJ2L2gwgxHr2Dr2UmGRaRyWxp3BfrfXX9xivmXsiIiIiIiIiZ4mzN2GQ08ptd5D94ce4Sqr3jHNVVpEw9Vq2HN5Fs6ZNadYiosbeeUcNHtmO1cv2MnB4Ct99s4vC/HKiYoOJj43EajPjsNc9U61jahPMFgOBQRYqK5x1tjMYwGq2cOe8B3F7jy2h3Zq/i9igKP466I80730ZFTtX4/W4CWrdHVNQGKagMKamX8MD85+g2F5a4779WqSTHNniBD+l+lmMDQ8ni9GMkdoP7jhbhNpCmN7/Ft7d/Cnf7fsep7v6+9IkJI4pqWNJiU7GZPxlMx1FRERERERE5NRQuCcAuKsqObRose9re8FhNhzx4DEaiAu2M+7aNL6dt5NNa7NxOavDtdj4EIaMak9wiA2vx4vD4Wb96v2ERwTw8bsbuHRcV3pd0IrvvtlV6zODQ20kdYjgox1f0rlnW1Z/m1Vn/87rEM/q3PV+wd5R+RWFPLvi30zrfwvh6RfXqI8OiuSpYX/iy12LWZyxgjJnBS3Cm3J5++F0ik85oT3mTkRyVBJmo7neZbcXtupNkDXwlDzv1xQWEMrkblcyvvNoyh3lWEwWrCYrobZg336HIiIiIiIiInLmKdwTAAyAu6J6aazBYiG0Xy+slVmkxLTG6DKzbPFu2rSLpd+Q86gotWM0GSgprmL10r106NoEl8tNeamDG+/qT+HhcrxeCAiykNY7CYfdzQ/L9uJ2HwvmYuJDuOSaDry68f/IKMri7/0vYPuGQ5QUVdXom9Vm5sJL2lJhKSF2TzT55TUP3thduJcyZ7nfibfuyjLwuPB6vYQaTVzV4WJGth2EAQMYIMwWeko/wwCTlYvbXsin2+fXWh8XHEPH+JRT+sxfU4AlgABLAOEBp/ZzEhEREREREZFTR+GeAOD1QmBiM6IvGU5Y/15sL8ygc3w78BgwYyMyKphd2/LJ3ldE+y4J5B0o5ev//ojD7iI3p5jf39mfFd/u4fXnl+FyVYd4JpORbj0TGXBRW7qlJ7J/7xG8Xi8JLcKoNJXxr81vsOdI9b5u/9zwJlNvnMKqeVns2HIQj9sLBmjdNpZ+I1vy9o73OVxZwD19/8Cj3z5PWS2n4O49sp+mofF4HFU4j+RSuOD/MEYmEtB1GEUlHpweEzFNIrAEWAj6ySEhpfYy3F4PJoOJ0JPcfy/AEsCl7Ybh8Xr4ate3uD1uX13rqCTu6fsHwmwh9dxBREREREREROTnMXi93vqPMxUAsrOzGTx4MAsXLiQxMfFMd+eUctsd2PPzsRs9bKzaz5oDm7kudTxeJ7grDcx+dTWlxf4z6jp2a0p63yRmvfo9LqeHiVN78cl7G2qdeXde+zgGDm/LVx9toXVKLJ36JHDTl9NqtIsOjGRE8mB6xHfB44awwGB+yN3AF5nzySnJA2BQcl8iAsL4aOtXNa6f1v9muse3p2r/NnLfeZTwS+4m40gki+fvpary2H5+LdtEc9nV3QgLrz51d2dBJp9un8/hikJigiIZ3e4i2kYnE3qSQVylswq7y8GOw3twuB20jmpJiC3olM8UFBERERERERHRzL1znLuqEldZBVgtFBorsBe7+X2niWxemUP+wTJCQm2MnZLGgf1FfPPJVt/S2h83HCA0PID0vi1Z+W0G5WV2LJbaD1nYte0Qg0a2o6LcyaG8sjqPkyioPMKsH+cy68e5APzjoum8uvFtvzbL9q3msSH31Qj3LCYLraOS8FSVc+iT5wnuOpiMI5F89WnN/f727i7grZdWcu1tfXhx3Vusz9viq8svL2Bb/m66JLTnjl7XnVTAF2gJINASwPnNu//sa0VEREREREREfg7tjH+OcpaWYS8sxFFUgtfjweBy09wSSSfLecx8bBGLvtzB5rU5rPw2g9efX0b+wVLGTO6BwXgsmlu3KovOqc3AAFExwZSW1Jy1d9SG7/fTe2AyKV3iMFuMDR5i0Ty8KYcrCmuUO9xOnG5njdNaL0sZihUTzqKDuMuOENB1JN8u2Fvn/Y8UVLB9ay5GQ+1R46a8bXy7dxVuT80DPEREREREREREzhYK985BzrIy8HhwV1VhtFowWCwYrVbcFZXExgRwxdiONa5Zs3wfRwoqaN85wVfmsLuoKHfQtn08h3JLcdjdNa47ym53ERxiJblNLG6vlwtb9am3j0NbD2BhxrJa66wmC57/hW42s42x7YZxYXAz7BsW4CrOxxQaTUmZm8oKZ63XH7V59QF6xHars/7zHQtq3dtPRERERERERORsoXDvHONxOvF6PHg9bgxGIyVbfqRg6TIq9mcDXjAYaNsuhtj4mvvDfb80gx69k/zKjEYDwy7rwPJFu+t9bvOWkTRrFYHDWIXL46R3i1Tax7aptW3v5qk0CY1nQ97WGnVJEYmEWIK4L+0aHux5Pc9fcBf9Ckopef9xStZ8jTEwDIPZisPhavCzcNhdWE3WOuuPVBbj8WrmnoiIiIiIiIicvbTn3jnG7XTidbkoWLGKQwsXEZbaA0NwCOWrf6Bs61aS//B7Aps1ZcrNPXnqLwv9ri0qrCQg0OL72hZgJjouBJPRgL2q7jDNFmCmTfs43tv2X5pHNKFDXFv+tXoW16eO41D5Yb7NXMmRqhLigqO5tN1FuDxunlnxCj8968WAgXGdRmFxOYlb+AG4XRQdOQj/C+BcxYcwmMyAl6iYEDAA9RwXk9AijJzy/XXWmwzGOpftioiIiIiIiIicDTRz7xzjdbko3bYdY3wzIm+4nY3GFFYcjiMjNp3Eu+6jaPde3BUVmKljxtpxWVfP/q1YsWgP+QfLGH99OraAmlmx2WJk3LVp2D1VzM/4jh8P7SSnOJe0pp15ePGzLM5cQe8WPRjbcSTdEjqwPGstobYQWoQ387tPbHA0d/a5Ho/HTcW6+Tjz9+MszPUFe0cVr/qY2BFTceXuoHXb2Lo/CAOk9W/BoqzldTbpmdgdi9FSa12ZvZziqlKKq0qpdNa916CIiIiIiIiIyK9JM/fONS4XxibNWbwwh13bj81a27MDvl++nyHDWxOUf4TwgMAal8bGh1BaVAUGSD2/BUnJUbz72g+sX72fm++7gD/cM4ANq/eze9shPB4vyW1jSeuThNHq5ZFlzwJgMpjYV5RDj6adyTiSxca8rWzL91/SGxkYzh29r6PcUUFeaT4htmA8Xg8bc7cxMul8itfNqPP1KnatISztYsJbtuGS5jZmvVlBwaGf7JtngFFjuxIUYqXKZa/1Pjazjau7XEqQ1f9zqHBUklOax3ubP2Vb/m5MRhM9m3ZhTKdLiAqMwGque5mviIiIiIiIiMippnDvHOMxmli/uYhd2w+T0CyM9l2aYAswU1JUxea1OSz4eg/Nru9KeC2rUQcMPQ+73cntDwzCXuXk9ZnLcbs9uN0eDmQXs3ntfgaP7ECXHol4vV7Kyxxk7Mxna+BasooPAJDatBPf7P6O+RlLue38axnaZgAL9yzjSFUJCcHRjGzejyhbGKWF+czL/p4iZxlWo5n+sV0Y1XYQxoyNeKrqP+TCFBSGNbopJnsFU27uTeauw6xdtR+H3UWzFhH0viCZoBArJouBx4fcz0ur32JP4T7f9a2jkri55ySiAyP97lvprGLpvu95fd37vjKXx8XSrB9Ymb2ehwbeQZuolphNGlYiIiIiIiIicnoohTjHuAwWtm87zDV/OB97lYtNa7OpKHcQHRvClZNSycspYcXyXEZfdSzYMpmNXDg8hYBAC2tWZLFr2yEGDU/B5Ty2JNbt8pCfV47T4ea155fhdlXXXXBJa3KcBwGID4mlSWgcOwsyAPj7dy/Qt3kaN3YbS/muPTj2ZVM065/kH8jFFhfLRUMGYkxIwVtaQdXiRQTd1h5jq64YrAF4HbUvhbXENMccGlXdb1sQwTbolJpIcrs4vB4vVpsZi8Xka58Y3oTpA27F7rJTYi8jzBaCzWwjzBZS494VzkreWPdB7Z+rx8VTy19hxvCHCDPVubiVTwAAJvRJREFUPIxEREREREREROTXoHDvHON0e7ns6u4s/Hwbe/cU+Mr3Zx5hw+r99B3Umo6pTfEYjfQb3IaQMBvtOiew8YdsZv97NXirT8gdckl7jEYDHo8XgwGi40JIahPFnh35vmDPYIA2HWN47bvtJEU04+aek/nX6v/z60+4LRiv24LRbiLvrXd95fZD+eS9M8f3dcspkzBYLRisVppc/RdyZz+M1+m/pNYUGk3C+D9hCg6v8d5BQXUvlw2zhYAthNjg6DrbeDweFmUsx1vPCR2l9jL2FefQOaBdnW1ERERERERERE6lc+pAjc8//5yRI0fSpUsXRowYwccff3ymu3Tamc0mNq/L8Qv2jrd80R5sAWZMRiMF+eXs2JLHws+3YTIbfSfPejxenA6375qUTgkUF1XSJTWR7+bv9JX3urAVJa4S7ut/E7/rfCkvfv8f9hZl++pNBiMjUobw7vxMcmzRxF12ea19iu7bm8geHcj/+Fkqd6/FHJFA81v+RdTgSQQkdSIwuRuxo26j2bX/wGCx4S4vwZ6XSVXOLlzlRXjslb/4c3N6nL6lxfXJLs79xc/6rfF6PbjLi3EU5FCVvRNX8WHclaVnulsnzOOoxFVeRFXOLux5GbjLi/G4HGe6WyIiIiIiIiLAOTRz78svv+See+5h8uTJ9OvXjwULFnD//fcTEBDA8OHDz3T3ThsDsGH1/nrbrF66l2ZXd2Xbpuqgymgy0P+itiz4bJuvjdFoxOPx0iQxnBGXd2Lv7gL+O2s9DrubkFAbfQYn0y4pCIPNyMtbP2Ttgc1+zzAZjNzU4zrmL8+jd+emrNiSS4c26XR8qh8lC+bhyMvDHBFB9PARBIVZOPjeQ3jtFVRmrMcal0STq/9CaNeLsMQ0x5m/n7Kty8n/7EUMVhthqcMIap1K3gd/x+txE9JpINGDJmAKCjvpz81sNBMZGNFguxNpcy7xOCqx52aQ/8W/cB05FnwGNO9A7OjbMIfHYDCcvf/G4K4oofDbdyjb9C1etxMAoy2I8D5XENb9IkyBNZdvi4iIiIiIiJxO50y4N2PGDEaMGMH06dMB6N+/P8XFxTz//PPnVLjndLpx2F31tjmwvwiv99jyU4/b67dPXYvkKIqOVDD22jTCwgNYu2of57WLY+Ifzicg2EKR8wil9kJy/vMulftzmHzvbVycfAHz966gymWnVXhLhrTpy8eL9/Hpt3uwmjN58vb+/PnlFZhNRgZ160VsWzNFVW6Wfbafxya2x2uv8D3fcWgfh+e/QVBKL/I/etqv715HFcWrPsFdUULUoIkUzHuNso0LcBXlEX/FPZiCTm4/PJPRxLDW/fl61+I629hMVtrHtD6p+/9WOQ5lkTv7YfB6/Mqr9m8l5837Sfz9s5hDImu/+AxzV5Zy6OPnqMzc6FfusVdwZPEsvPYKIvpcjtEWdIZ6KCIiIiIiInKOLMvdv38/WVlZDB061K982LBhZGRksH9//TPZfkuMtZyC+1Nmiwk8x8K9kFDbcXVGhl3akcMHy1g6fxevPbeM777Zxeszl/PmiyvYszeXf2+eRYQpkIrMvdgPHWLPvX8mfOlWRrW8nDaeCzm4vQkr1xVy6HD10kaHy8M332cxOL0FR0rtfLh0Ly9/tZv3FmeSfagMDzU7Xb5tJdaoplDHrK+yTd8SkJiC4X/BS9W+LTiLDv6cj6qGUIOZgS3S66y/psNIrN669+Q717grSjg87981gr2jPBUllKz5Cs//ZsSdbVzF+TWCveMVrfoEz0/2fRQRERERERE53c6JcC8jo/p01latWvmVJyUlAZCZmelXXlJSQnZ2tt+vvLy809PZX5nJZCAmrv6lhB07x2ExHttTL7VXC1wuN63Oi+GaP/Ri1ZIMvvpoC7nZxX7XeTxevF5IimiG+dARHIVHfHXOslKyDpTz9ue7WfhDNj9sO0i7lsdmbK3bfpB2SVE1+hIVFgD28pqd9LhxlxVirHNZpJeKXWsJbNnJV1Kydh4e18kFSR63C/vy/zK+WU8mdhxFRMCxJb7NQhP4Y9pEujtNuDLqDoPONV63C0deZr1tSjctxlNVUW+bM8HjdlGydl5DjajM3HR6OiQiIiIiIiJSh3NiWW5pafXm/SEh/kFQcHAwAGVlZX7lb731Fi+++OLp6dxpZsXJ4GHJvP927aGELcBMr/4tfTP8kpKj6JTaDFuAmQsvbsvnH2zmUG4dhyEYoHlSFMmegWQ9/KRfVezgwbz8+T7f1z+d4FbXhLdRvZvh3vx17Y+z2PDWE9Z57BUYzNbjvi4Hjxuw1HlNndxuPBXFlL7zKOkd+9M7/To8FhsGgwFKj+D64SvKdq3BNvT6n3/v3yivu/7l3wCeqopa5mWeBTxuPFW1hMo/0ZgOBhEREREREZHfpnMi3PM2sFTSaPSfwDh58mQuv9z/5Na8vDwmTJhwyvt2uhmApk2CuXxMe+Z9uYeK8mOnfsbEh3D5Ve2x4cDrtjDyqs7ENw3DajORU3WAJhEJFB+p++TZth3jCQQyH32aqtxjhyeEd+2KMzCE7XuPzeTr1jaW3fuL/L/OPvY1QNc2UVzYPpSS2ctqPMsUFgNeL15H3f2xxiVRtf/YISABiSl+Yd/PYbBYsCW2o3z7Kio2LYZNte+9Z2va5qTu/1tktNiqA9h6lq5a45PqXFp9JhnMFgIS21G+fWW97QKanneaeiQiIiIiIiJSu3Mi3AsNrT5EobzcfybO0Rl7R+uPCgsLIyzs5E9WPZsZrFbsP24juWVLbrixK4UFlZSXOYiODSbA7MVmdeF1AOFh2KsOExpmxUMFARm5FO3fyDWTz+edWZupLPefMde8VSQXX3IeO6bdj+Pw4epCo5Ho/v1oMWUy9762ztc2ONBCj3ZxzP66OngzmwxcfkFrXvv0R2IjAomJCOSyAa1IiXJS9uEjvlNKjxc9eBIlGxfV+Z7GoDAsMc2wH9j1vwIzIZ0GYDCeXJBkMBgJ6diPwsWzoI4ZaebwOCyR8Sd1/98igyWA0C4XUrK29pmXAJH9xpz0ISe/JoPBSHDHfhQsfrue73cslsiE09wzEREREREREX/nRLh3dK+9rKwsUlJSfOX79u3zqz8XmAMDCevQjtwvv8YcFER8ancMUUF4HHbylywlrH07Qtu3o7zMTZcezajctA4DEBUbS0WZgwBPGTfdM4A9uwrYt7sAs8VI1/TmlDndYDLQ5anHKc3ch8fjITS5FTlH7Nz58g/kFlTvqxYeYmXapHRmfb0dl9tLgNXEA1d3wpqxlJsvTMYY2gZH9jbMeV8TEnchVZHxuMuLfP03BoURPeRaAlt2wRQSRcXW5TXCP4MlgLjL7qJo2dz/FRiJv+pejNZfdqqp0RZE/FX3cXDOE/9b3ntcXUAwCeMewBh49gVVZ4rRaiOy/1iqcnbiyMuoUR/aYxi2s3jmm9EWSPxV93Nwzj/q+H7/qZ49H0VEREREREROD4O3oTWrvxGDBw+mW7duPPPMM76yO++8k23btjFvXgMb5wPZ2dkMHjyYhQsXkpiY+Gt29bSwFxbidTg5uGgRzsIiglu1JLp3L7xGE3aDBYvJizMnm+y5H1KZnUNAfDwJo0ZCsyRmfLyDhOgg+nVpSvP4EHZlFdA2IRBT7maCk7vgLC3CHBpFSUk5JQ4jizYepsrpITUllpSkKNZtP8T2rCO0axZEausI3JmrCW3eFnNEHM6CAxQt/xBX0UFsTVOI6HMZBrMF55E8jLZgLBFxGGxBGM0WPE4HnsoSin/4kordawEDQSk9CetyISXr51Oxaw2BLTsT3ms0pqAIjFZbg59LQzzOKtzlJRSv+pTKvZvAaCKkQz9Cuw3GFBiKwWT65d+c3xh3RQmVezdT/MMXeCpKsEQ1I6LvFViim2I6y8NQ3/f7+8+qT87V91tERERERETOMudMuPfRRx8xffp0JkyYwMCBA1m4cCHvvfceM2bM4OKLL27w+t9auHc8t7t6VpLT6cFrAJMRSitc2CwmsFdi9npwe8EQHEyV3YMXD14v2MwGXC43wRY3BpcDgyUQr8eF02DDiwGrtwqDwYDBbMHrNWA0GTGYrXgdVXgxgMeJ1+PGYLJgCgg+1p+qMrxuFwaD6YSWbHpcjurDMzBgCAjCgAFPVTlevBitgRgtvzzUq/FMpx2Po/J/zwzGaDonJsH+Iu6KErxeT43vd2Og77eIiIiIiIicrc6Zv6FeccUVOBwO3njjDebMmUPz5s154oknTijY+60z/W/2kem4WUjR4f87UTbI/wCKoICG73fsLNraGxtsgfXWmwJ+3lJHo9mK8ScHZZiCw3/WPX4uo8X2q4SGv2WmoMa7j6W+3yIiIiIiInK2OmfCPYDx48czfvz4M90NERERERERERGRU+Lkjg4VERERERERERGRM07hnoiIiIiIiIiISCOlcE9ERERERERERKSRUrgnIiIiIiIiIiLSSCncExERERERERERaaQU7omIiIiIiIiIiDRSCvdEREREREREREQaKYV7IiIiIiIiIiIijZTCPRERERERERERkUZK4Z6IiIiIiIiIiEgjpXBPRERERERERESkkVK4JyIiIiIiIiIi0kgp3BMREREREREREWmkFO6JiIiIiIiIiIg0Ugr3REREREREREREGimFeyIiIiIiIiIiIo2Uwj0REREREREREZFGSuGeiIiIiIiIiIhII6VwT0REREREREREpJFSuCciIiIiIiIiItJIKdwTERERERERERFppBTuiYiIiIiIiIiINFIK90RERERERERERBophXsiIiIiIiIiIiKNlMI9ERERERERERGRRkrhnoiIiIiIiIiISCOlcE9ERERERERERKSRUrgnIiIiIiIiIiLSSCncExERERERERERaaQU7omIiIiIiIiIiDRSCvdEREREREREREQaKYV7IiIiIiIiIiIijZTCPRERERERERERkUZK4Z6IiIiIiIiIiEgjpXBPRERERERERESkkVK4JyIiIiIiIiIi0kgp3BMREREREREREWmkFO6JiIiIiIiIiIg0Ugr3REREREREREREGimFeyIiIiIiIiIiIo2Uwj0REREREREREZFGSuGeiIiIiIiIiIhII6VwT0REREREREREpJFSuCciIiIiIiIiItJIKdwTERERERERERFppBTuiYiIiIiIiIiINFLmM92BxsLtdgOQl5d3hnsiIiIiIiIiIiLnmoSEBMzmmlGewr0TlJ+fD8CECRPOcE9ERERERERERORcs3DhQhITE2uUG7xer/cM9KfRqaqqYsuWLcTGxmIymc50d06ZvLw8JkyYwOzZs0lISDjT3RFpNDR2RE6Oxo7Iz6dxI3JyNHZETo7GztlLM/d+oYCAANLS0s50N341CQkJtaa/IlI/jR2Rk6OxI/LzadyInByNHZGTo7HTeOhADRERERERERERkUZK4Z6IiIiIiIiIiEgjpXBPRERERERERESkkVK4d44LCwvj1ltvJSws7Ex3RaRR0dgROTkaOyI/n8aNyMnR2BE5ORo7jY9OyxUREREREREREWmkNHNPRERERERERESkkVK4JyIiIiIiIiIi0kgp3DuHff7554wcOZIuXbowYsQIPv744zPdJZGzxrZt2+jYsSN5eXl+5cuWLePKK6+ka9euDBo0iDfeeKPGtZs3b2bixIl0796dfv368eyzz+J0Ok9X10VOK4/Hw7vvvsuoUaPo3r07Q4YM4fHHH6esrMzX5kTGxN69e5k6dSppaWmcf/75/OUvf/G7h8hvjdfr5T//+Q/Dhg2jS5cujB49ms8++8yvjX7miDTs1ltv5aKLLvIr09gRqcnlctGlSxdSUlL8fnXv3t3XRmOn8TKf6Q7ImfHll19yzz33MHnyZPr168eCBQu4//77CQgIYPjw4We6eyJn1J49e/jDH/6Ay+XyK1+3bh1Tp05lxIgR3HHHHaxdu5Ynn3wSr9fL9ddfD8C+ffuYMmUK3bt357nnnmPPnj3MmDGDsrIyHnrooTPxOiK/qtdee43nnnuO66+/nt69e5OZmcnMmTPZvXs3r7/++gmNieLiYiZPnkxsbCxPPPEEBQUFPPXUU+Tl5fHKK6+c4TcU+XW88sorzJw5k9tuu41u3brx3Xffcc8992Aymbj44ov1M0fkBHzyySfMnz+fFi1a+Mo0dkRql5mZid1u54knnqBly5a+cqOxes6Xxk4j55Vz0pAhQ7x33nmnX9kdd9zhHT58+BnqkciZ53Q6vbNmzfJ2797d27NnT2/btm29ubm5vvrJkyd7x4wZ43fNk08+6U1LS/Pa7Xav1+v1PvDAA94LLrjA97XX6/XOnj3b2759e29eXt7peRGR08Tj8XjT09O9Dz/8sF/5F1984W3btq1369atJzQmXnrpJW+3bt28hYWFvjbffvutt23btt4NGzacnpcROY0cDoc3PT3d+8gjj/iVX3PNNd7f/e53Xq9XP3NEGpKXl+dNT0/3DhgwwDtkyBBfucaOSO0+/fRTb7t27bwVFRW11mvsNG5alnsO2r9/P1lZWQwdOtSvfNiwYWRkZLB///4z1DORM2vt2rU8/fTTXHfdddxzzz1+dXa7nTVr1tQ6bkpKSli3bh0Ay5cv58ILL8RqtfraDB8+HLfbzbJly379lxA5jcrLyxk9ejSXXHKJX3lycjIAWVlZJzQmli9fTnp6OpGRkb42/fr1Izg4mCVLlpyGNxE5vUwmE2+//TY33nijX7nFYsFut+tnjsgJePDBB+nbty+9e/f2lWnsiNRt27ZttGjRgsDAwBp1GjuNn8K9c1BGRgYArVq18itPSkoCqqfripyLWrduzYIFC7j11lsxmUx+dfv378fpdNY7biorK8nNza3RJioqipCQEI0t+c0JCQnhwQcfpEePHn7lCxYsAKrH1ImMiYyMjBptTCYTiYmJGjfym2Q0GklJSSE+Ph6v18vhw4d59dVXWbFiBePGjdPPHJEGzJkzhx9//JE///nPfuUaOyJ127FjB1arleuvv57u3buTnp7OQw89RFlZmcbOb4D23DsHlZaWAtV/KTtecHAwgDYwl3NWTExMnXUnMm7qanO0ncaWnAs2btzIq6++ypAhQwgLCwMaHhOlpaUaN3LO+uabb7j99tsBGDhwIKNHj2bbtm2AfuaI1CYnJ4fHH3+cxx9/nKioKL86/XlNpG7bt2+nrKyMMWPGMHXqVLZs2cILL7xAZmYmd999N6Cx05gp3DsHeb3eeuuPbqgpIsecyLjR2JJz3dq1a5k6dSqJiYk89thjOByOetufyJjQuJHfug4dOjBr1ix27NjB888/z4033sgdd9xR7zX6mSPnKq/XywMPPMAFF1zAsGHDaq2vj8aOnMtmzJhBeHg4KSkpAKSnpxMdHc29997L8uXL671WY+fsp3DvHBQaGgpU75V0vKNJ+9F6ETnmRMbN0X/F+mmbo+00tuS37Msvv2TatGm0bNmS1157jcjISN9YaGhMhISE1NmmadOmv27HRc6w5s2b07x5c9LT0wkJCeH+++/31elnjoi/2bNns2PHDj777DNcLhdwLNBzuVz685pIPXr27FmjbODAgX5fa+w0XopWz0FH18hnZWX5le/bt8+vXkSOadGiBSaTqca4Ofp1q1atCA4OJj4+3jeWjiooKKC8vFxjS36z3nzzTe6++266devG7NmziYuLAzjhMdGqVasabdxuN9nZ2Ro38ptUVFTExx9/zMGDB/3KO3ToAEB2drZ+5ojUYt68eRw5coR+/frRsWNHOnbsyMcff0xWVhYdO3ZkzZo1GjsitSgoKGDOnDk1Ds+sqqoCIDo6WmOnkVO4dw5KSkoiMTGRr7/+2q/8m2++oWXLlpolIVILm81GWloa33zzjd+U9Hnz5hEaGkqnTp0A6Nu3L4sXL/Zbjjhv3jxMJlOt/1om0tjNmTOHf/zjH4wYMYLXXnutxr/ansiY6Nu3L99//z1FRUW+NsuWLaOiooI+ffqclvcQOZ08Hg/Tpk3j/fff9ys/uiyqc+fO+pkjUou//vWvzJ071+/XhRdeSEJCAnPnzmX48OEaOyK1MBgMPPTQQ8yaNcuv/Msvv8RkMtGnTx+NnUZOy3LPUbfccgvTp08nPDycgQMHsnDhQr766itmzJhxprsmcta66aabuPbaa7nrrru4/PLLWb9+Pa+//jp//OMffUfK33DDDXzxxRfceOONTJ48mb179/Lss88yduxYBefym1NQUMDf/vY3mjVrxoQJE9i6datffYsWLU5oTFx99dXMmjWLKVOmcMstt1BUVMRTTz3FgAEDSE1NPROvJvKrioqK4uqrr+bVV18lICCAzp07s3btWl555RXGjBlDcnKyfuaI1CI5OblGWUREBFarlc6dOwP685pIbaKiopgwYQJvv/02ISEhpKWlsXbtWl5++WUmTJhAUlKSxk4jZ/A2tCui/Ga99957vPHGG+Tm5tK8eXNuvPFGLrvssjPdLZGzwkcffcT06dNZsmQJCQkJvvL58+czc+ZMMjMziY+PZ8KECVx33XV+165Zs4Ynn3ySbdu2ERkZyWWXXcZtt92GxWI53a8h8qv6+OOP/fYH+6knn3ySSy+99ITGxM6dO/n73//O+vXrCQ4OZsiQIdx33321nsgm8lvgdDr5z3/+w9y5czlw4AAJCQmMHTuW66+/3rcpuX7miDRs2rRprF27lvnz5/vKNHZEajr6c+fDDz8kJyeH+Ph4xo4dyw033KCfO78BCvdEREREREREREQaKe25JyIiIiIiIiIi0kgp3BMREREREREREWmkFO6JiIiIiIiIiIg0Ugr3REREREREREREGimFeyIiIiIiIiIiIo2Uwj0REREREREREZFGSuGeiIiIyM8wbdo0UlJSmD17dq312dnZpKSk8MILL5zWfqWkpDBt2rTT+syfy+FwMH36dFJTU0lNTWXRokW1tjv6Gdf3q6SkBICJEyfWqOvSpQvDhg1jxowZ2O320/mKP0tZWRmFhYVnuhsiIiLSyJnPdAdEREREGqPnnnuOYcOGERMTc6a70mh88MEHfPTRR1x66aWkp6fTqVOnettPnz6dyMjIWusCAwP9vn7yyScB8Hq9VFVVsWXLFv7973+TmZnJzJkzT80LnEJbtmzhpptu4umnn+b8888/090RERGRRkzhnoiIiMhJKCkp4fHHH+eZZ545011pNHbs2AHAQw89REhISIPthwwZQmJi4gnd+9JLL61RFh4ezmuvvcbOnTtp27btz+vsr2znzp0cOnToTHdDREREfgO0LFdERETkJAwaNIjPP/+clStXnumuNBpOpxPghIK9U+HojLjdu3eflueJiIiInAkK90REREROwoMPPkhgYCAPP/wwDoej3raDBg1i4sSJDZYPGjSIRx55hDlz5jBs2DC6dOnClVdeyaZNm8jPz+eOO+6ge/fu9O/fn2effRaPx1Pjni+//DL9+/ena9euTJo0iU2bNtVos3jxYsaPH0/Xrl1JT0/ntttuIzMz069NSkoKzz33HFOnTqVTp06MHDkSl8tV5zsuWLCA8ePH06VLF9LS0pg6dSrbt2/3u99///tf3+9r+zxOtby8PACaN29eb7tBgwbxpz/9iTlz5jB48GC6devG+PHjWbVqVY22a9asYcqUKXTv3p3u3bszadIkfvjhB782xcXFTJs2jYEDB9KpUyeGDBnCM88849v/74UXXmD69OkATJo0iUGDBgHVS4pffPFFhg0bRufOnenTpw/33nsvubm5v/izEBERkd8uhXsiIiIiJ6FZs2bcfPPN7N27l1dfffWU3XfBggU8//zzXHXVVdx6661kZGRw2223ce2112I0Gpk2bRpt27bllVde4ZNPPvG7dt68ebz55puMHz+eW265hYyMDCZNmsSuXbt8bT766CNuuukmAgMDuffee5kyZQrr169n7NixNQK+t956C6fTyYMPPsiYMWMwm2vf0WX27NnccsstOJ1O7r77bqZMmcKmTZv43e9+5wsXn3zySdLS0ny/nzp1aoOfRUlJCYWFhTV+1RZqHl+fl5fHggULmDlzJgMHDqRz584NPmvFihU88sgjDBs2jDvuuIPCwkJuuOEGVq9e7WuzcOFCJk6cSG5uLjfddBM33XQTubm5TJkyhYULF/ra3XnnnSxevJgxY8bwl7/8hZ49e/Lqq6/y2GOPAXDRRRcxbtw4AKZOncoDDzwAVAezL730Ev379+ehhx5izJgxLFiwgOuuuw63293gO4iIiMi5SXvuiYiIiJyka6+9lk8//ZRXX32VUaNGkZSU9IvveejQIT755BNSUlIAKCoq4vXXXyc1NZUZM2YAMGrUKHr27MmyZcu4/PLLfdfa7Xbee+8937XDhg3j4osvZubMmbzwwguUlZXxt7/9jYsvvphnn33Wd93YsWMZOXIkTz/9NC+99JKv3Gw289JLLxEQEFBnf48cOcJTTz1Fly5dmD17NlarFYDLLruMSy65hEceeYS5c+dy6aWXsnLlStasWVPr/ni1Of7djrdw4cIae/H17t27Rrvo6GhfcNaQAwcO8NJLLzFkyBCgeg+/YcOG8cwzz/D+++/jcrl45JFHiI+P58MPP/QtLR4/fjyXXHIJf/3rXxkwYAAlJSWsWLGC++67j+uvvx6AMWPG4PV62b9/PwDt2rWjW7duvP/++/Tp08e3fPizzz5jwIABPPjgg75+NWnShHfffZecnBxatGhxQu8iIiIi5xaFeyIiIiInyWKx8PDDD3PNNdfwyCOP8Prrr//ie7Zo0cIXzgG0atUKqJ7tdVRQUBDR0dHk5+f7Xdu/f3+/a5OSkhgwYADLli3D7XazfPlyysrKGDJkCIWFhb52JpOJXr16sWTJElwul2+GXpcuXeoN9gBWrlxJZWUl1157rS/YA0hMTGT06NG8//77HDp0iLi4uJ/9WTz11FO1nkYcGxtbo+zNN9/0/b6qqop9+/bxn//8h6uuuoq3336bdu3a1fus5ORkX7AHEBUVxaWXXsqsWbMoKCggJyeHvLw87rnnHr89A8PCwrjmmmt45pln2LJlCx07diQoKIh33nmHxMRE+vfvT1BQEI8//niD75uQkMD333/PW2+9xciRI4mJiWH8+PGMHz++wWtFRETk3KVwT0REROQXSEtL4/LLL+ejjz7iiy++oGvXrr/oftHR0X5fm0wmoDps+mm51+v1K0tOTq5xvxYtWrBo0SIKCwvJysoC4K677qrz+YWFhb4g7qfPrE12dnadz27dujVQPSvuZMK91NTUEz4tt0+fPjXKBg4c6JuR+Nprr9V7fZs2bWqUJSUl4fV6ycnJ8b3n0bD1eEff/cCBA3Tv3p1HHnmEP//5z9x+++1YrVZ69uzJ0KFDueyyy7DZbHX24b777uOmm27i73//O48//jgdO3Zk0KBBjB07ttZAU0RERAQU7omIiIj8Yvfeey+LFi3i8ccfbzBEOl5t+6jVta+dwWA4qb4dDQBNJpNvr7pHH320ztAsPDzc9/ujweLJOvpsi8Xyi+5zslq1akVKSgrr169vsG1tfTz6/aktSD3eT99z1KhR9O/fnwULFrBkyRJWrFjBsmXLeOedd5gzZ47fDMfjtWvXjnnz5rF06VIWL17M0qVLmTlzJm+++Sbvv/++LywVEREROZ4O1BARERH5haKiorjnnnvIz8/nueeeq1FvNBprnKjrcrk4cuTIKe1HTk5OjbK9e/cSGhpKZGQkzZo18/W3T58+fr9MJhMGg6HO4KkuR++ZkZFRo+5oWUJCws99lVPG4/FgNDb8R96jsxqPt2/fPkwmE4mJifW+59GDSBISEigvL2fNmjUYDAauuuoqXnjhBVauXMmkSZPYvn07y5Ytq/X5brebH3/8kdzcXAYPHsxjjz3GkiVLmDFjBqWlpcyZM+fnvLaIiIicQxTuiYiIiJwCV111FampqSxevLhGXUxMDJmZmVRVVfnKFi1ahN1uP6V9WLp0KQcPHvR9vXPnTpYtW8agQYMwGAz06dMHm83Ga6+9htPp9LU7ePAgN998M08//fTPniF49J5vvvmmX4CZl5fHZ599RpcuXWosNT5ddu3axc6dO+nZs2eDbTdv3syGDRt8Xx8+fJhPP/2UXr16ER4eTseOHYmNjeXdd9+lrKzM166srIx33nmH2NhYOnXqxK5du5gwYQJz5871tbFarXTo0AE4NhvyaOB4dDal2+1m0qRJ/P3vf/fr19Fl3icSUIqIiMi5SctyRURERE4Bg8HAww8/zBVXXIHL5fKru+SSS3j00Ue54YYbGD16NPv27eODDz7wzQY7VaxWK1dffTUTJ06koqKCt956i7CwMO68806gesbe3XffzeOPP864ceMYPXo0LpeLd955B7vdzv333/+znxkZGem75+9+9ztGjRpFeXk57777Lh6Px+/k11/TJ5984vu91+slMzOTDz74AKvVym233dbg9Varld///vdMnjyZgIAA3nnnHTweD/fddx9QveT2wQcf5K677uLKK6/kqquuAmDu3LkcOnSImTNnYjQa6dq1K2lpacyYMYPc3FxSUlLIzc1l1qxZJCcn+071Pbqf4bvvvsvhw4cZNWoUEydO5F//+he33HIL/fv3p6qqivfff5/AwECuvPLKU/2RiYiIyG+Ewj0RERGRUyQlJYVJkybxxhtv+JVfffXVFBUVMXfuXB599FHatWvHiy++yBtvvEFFRcUpe/64ceMwGAy8/PLL2O12zj//fKZNm0bTpk19baZMmUJ8fDxvvvkmM2bMICAggI4dO/LUU0/Ro0ePk3rulClTiIuL44033uDZZ58lMDCQnj17cuutt/qd3vtrOhrCQfW+hZGRkaSnp/OHP/yhwZNyAbp168bIkSP55z//SWlpKWlpafzxj3/0u3b48OGEh4fzz3/+k5deegmz2UzXrl3529/+RlpaGlAd8r700ku8+OKLLF68mPfff5/w8HCGDh3KHXfc4Vv23Lt3b0aMGMHixYtZtWoVQ4cO5fbbbyciIoIPP/yQJ554ApPJRGpqKk899ZT22xMREZE6Gbz17Q4sIiIiIvIbN2jQIJo1a8bbb799prsiIiIi8rNp8w4REREREREREZFGSuGeiIiIiIiIiIhII6VwT0REREREREREpJHSnnsiIiIiIiIiIiKNlGbuiYiIiIiIiIiINFIK90RERERERERERBophXsiIiIiIiIiIiKNlMI9ERERERERERGRRkrhnoiIiIiIiIiISCOlcE9ERERERERERKSR+n+++ap6AcwTCwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('### Number of Tweets vs FB posts by News Outlet'))\n", + "\n", + "sns.set(font_scale=1.5)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "df = by_url.merge(news[['news_url', 'outlet']].drop_duplicates().set_index('news_url'), how='left', left_index=True, right_index=True)\n", + "ax = sns.scatterplot(x='num_posts_fb', y='num_posts_tw', hue='outlet', data=df, s=100)\n", + "\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False) \n", + "\n", + "ax.legend(title='Outlet')\n", + "ax.set_xlabel('Number of FB posts')\n", + "ax.set_ylabel('Number of Tweets')\n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/Social media posts of news stories by outlet.png')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_style(\"ticks\")\n", + "\n", + "# plt.figure(figsize=(18,16))\n", + "\n", + "df = by_url.merge(news[['news_url', 'outlet']].drop_duplicates().set_index('news_url'), how='left', left_index=True, right_index=True)\n", + "\n", + "\n", + "x, y, hue = df['num_posts_fb'], df['num_posts_tw'], df['outlet']\n", + "g = sns.JointGrid(height=12)\n", + "\n", + "sns.scatterplot(x=x, y=y, hue=hue, s=70, linewidth=1.5, ax=g.ax_joint)\n", + "g.ax_joint.legend(title='Outlet', markerscale=.8)\n", + "g.ax_joint.set_xlabel('Number of FB posts')\n", + "g.ax_joint.set_ylabel('Number of Tweets')\n", + "\n", + "sns.boxplot(x=x, showfliers=False, linewidth=1, ax=g.ax_marg_x)\n", + "sns.boxplot(y=y, showfliers=False, linewidth=1, ax=g.ax_marg_y)\n", + "# sns.histplot(x=x, fill=False, linewidth=2, ax=g.ax_marg_x)\n", + "# sns.kdeplot(y=y, linewidth=2, ax=g.ax_marg_y)\n", + "\n", + "def format_axis(ax):\n", + " for s in ax.spines.values():\n", + " s.set_color('lightgrey')\n", + " s.set_linewidth(1)\n", + " ax.tick_params(color='lightgrey', width=1)\n", + " \n", + "format_axis(g.ax_joint)\n", + "format_axis(g.ax_marg_x)\n", + "format_axis(g.ax_marg_y)\n", + "\n", + "# for s in g.ax_joint.spines.values():\n", + "# format_spine(s)\n", + "\n", + "# format_spine(g.ax_marg_x.spines['bottom'])\n", + "# format_spine(g.ax_marg_y.spines['left'])\n", + "# (x='num_posts_fb', y='num_posts_tw', hue='outlet', data=df, s=100)\n", + "\n", + "\n", + "# g.plot_joint(sns.scatterplot)\n", + "# g.plot_marginals(sns.boxplot)\n", + "\n", + "# ax.spines['right'].set_visible(False)\n", + "# ax.spines['top'].set_visible(False) \n", + "\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/Social media posts of news stories by outlet.png')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Number of unique research articles (DOIs) covered by outlet that month" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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kJPgsc7lcSk5OrqgWg0aEI1y9R87xq/atNOu+PmF2hzan9fe7vvnIGQHspvwZGV/zkTPUd9Ywv2pf7zf1j7RV4SLsDsuODcHF6O8cTgmPcGh5n35+1XZ+Y1aAuwEA+MNSYXLfvn16+OGHlZ6erpiYmDJrnE6nnE5nBXcGAAAAANZiqTA5c+ZM5efna8qUKd5lPXv2VK9evUzsCgAAAACsx1JhMjU1VampqWa3AQAAAACWZ6n7TAIAAAAAKgZhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYJglw2ReXp66du2q3Nxcs1sBAAAAAEuyXJjcunWrevXqpZycHLNbAQAAAADLslyYnDt3rsaPH6/IyEizWwEAAAAAy7Kb3UB5mzRp0lnXu91uud1un2UulyuQLQEAAACA5VguTJ5LZmamMjIyzG4j5JQUFSrM7ij32lBUUFSoCD/HZ6QWwaO4oFDhEf69b0ZqJamgsFgRjvByrw0GVh5boITi90mofccHw+cykN8pgRIMn02+J4Dgd8GFyZSUFCUkJPgsc7lcSk5ONqmj0BBmd2hzWn+/apuPnBHgbswVYXeo76xhftW+3m9qgLtBIIRHOLS8Tz+/aju/McvQviMc4eo9co5ftW+lhdb3kpXHFiih+H0San8fBMPnMpDfKYESDJ/NYHjvAJzdBRcmnU6nnE6n2W0AAAAAQEiz3AV4AAAAAACBZ9mZybVr15rdAgAAAABYFjOTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDLBcmlyxZos6dO6tDhw6aM2eO2e0AAAAAgCXZzW6gPO3fv1/p6elasGCBIiIi1LNnT7Vs2VLXXXed2a0BAAAAgKVYKkxmZ2erVatWql69uiTprrvu0ooVKzR48GBvjdvtltvt9tnuxx9/lCS5XK4K6zVY5B8/5lddbm6uDv5y0u/aUOPv2KRT4zt57LjftYESqPcuGMZmRKDGdiQ/cJ93I++dEcHw3gVqbKHGyr9zUuj93gXD71wgv1MCJRg+m1b5uy46Olp2u6X+2Q1Ikmwej8djdhPl5Z///KeOHz+uESNGSJLmzZunbdu26cknn/TWTJs2TRkZGWa1CAAAgAvMmjVrVLt2bbPbAMqdpf6LpKxcbLPZfB6npKQoISHBZ1lBQYH27t2runXrKjw8PKA9SqdmQJOTkzVnzhxFR0cH/PkqkpXHJll7fIwtdFl5fIwtdFl5fFYem2Tt8Zk1Nqu9jsBplgqTUVFR2rRpk/fxgQMHFBkZ6VPjdDrldDpLbXvNNdcEvL/fi46Otuz/Ull5bJK1x8fYQpeVx8fYQpeVx2flsUnWHp+VxwZUJEtdzfXWW2/Vhg0bdOTIEZ04cUIrV65U69atzW4LAAAAACzHcjOTI0aMUJ8+fVRYWKikpCQ1btzY7LYAAAAAwHIsFSYlKS4uTnFxcWa3AQAAAACWZqnDXEOF0+nU4MGDyzx3M9RZeWyStcfH2EKXlcfH2EKXlcdn5bFJ1h6flccGmMFStwYBAAAAAFQMZiYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJiUVFRUpNzdXRUVFZrcCAAAAACGBMCnJ5XKpffv2crlcZrcCAAAAACGBMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAABgUEFRYUBqEXi8d0D5sZvdwPmaOnWqPvjgA9lsNiUlJalfv37Kzs7W5MmTlZ+fr06dOmnEiBFmtwkAACwowu5Q31nD/Kp9vd/UAHcDI3jvgPITkmFy48aN+uSTT7R48WIVFRWpc+fOiomJ0dixYzV79mzVrFlTDz74oNatW6c2bdqY3S4AAAAAWE5IHubaokULvfHGG7Lb7Tp8+LCKi4vldrt19dVXq06dOrLb7YqLi9OKFSvMbhUAAAAALCkkZyYlyeFw6MUXX9Rrr72mjh076sCBA6pRo4Z3fWRkpPbv319qO7fbLbfb7bPM5XIFvF8AAAAAsJKQDZOSNHToUA0YMEAPPfSQcnJySq232WyllmVmZiojI6MCugMAAAhNBUWFirA7yr0WgLWEZJj8/vvvVVBQoBtuuEFVqlRRbGysVqxYofDwcG/NgQMHFBkZWWrblJQUJSQk+CxzuVxKTk4OeN8AAAChgIvUAPBHSJ4zmZubq9TUVBUUFKigoEBr1qxRz549tXv3bu3Zs0fFxcVaunSpWrduXWpbp9Op2rVr+/yJjo42YRQAAAAAELpCcmayTZs22rp1q+6++26Fh4crNjZWXbp00WWXXaYhQ4YoPz9fbdq0UceOHc1uFQAAAAAsKSTDpHTqfMmhQ4f6LIuJidHixYtN6ggAAAAALhwheZgrAAAAAMBchEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYXazGzhfGRkZev/99yVJbdq00ciRIzVmzBht3rxZVapUkSQNHjxYHTp0MLNNAAAAALCkkAyT2dnZWr9+vbKysmSz2dS/f3+tWrVK27dv15tvvqnIyEizWwQAAAAASwvJw1xr1Kih0aNHKyIiQg6HQ9dee61++ukn/fTTT3riiScUFxenF198USUlJWa3CgAAAACWFJIzk/Xr1/f+nJOTo+XLl+utt97Sxo0bNXHiRFWtWlUPPvig5s+fr3vuucdnW7fbLbfb7bPM5XJVSN8AAAAAYBUhGSZP+/bbb/Xggw9q1KhRuuaaa/TSSy951913331auHBhqTCZmZmpjIyMim4VAAAAACwlZMPk5s2bNXToUI0dO1ZdunTRzp07lZOTo7vuukuS5PF4ZLeXHl5KSooSEhJ8lrlcLiUnJ1dI3wAAAABgBSEZJvft26eHH35Y6enpiomJkXQqPD799NNq1aqVqlatqnfffbdUaJQkp9Mpp9NZ0S0DAAAAgKWEZJicOXOm8vPzNWXKFO+ynj17auDAgerVq5eKiooUGxurrl27mtglAAAAAFhXSIbJ1NRUpaamlrmOw1UBAAAAIPBC8tYgAAAEq5KiwoDUAgAQbEJyZhIAgGAVZndoc1p/v2qbj5wR4G4AAAgcZiYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAEBAFBi4wJCRWgBAcOACPAAAICAi7A71nTXMr9rX+00NcDcAgPLGzCQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAsJANkxkZGerSpYu6dOmitLQ0SVJ2drbi4uIUGxur9PR0kzsEAAAAAOsKyTCZnZ2t9evXKysrSwsXLtSXX36ppUuXauzYsZo+fbqWL1+u7du3a926dWa3CgAAAACWFJJhskaNGho9erQiIiLkcDh07bXXKicnR1dffbXq1Kkju92uuLg4rVixwuxWAQAAAMCS7GY3cD7q16/v/TknJ0fLly/Xfffdpxo1aniXR0ZGav/+/aW2dbvdcrvdPstcLlfgmgUAAAAACwrJMHnat99+qwcffFCjRo2S3W7X7t27fdbbbLZS22RmZiojI6OiWgQAACGipKhQYXaH2W0AQMgI2TC5efNmDR06VGPHjlWXLl20ceNGHTp0yLv+wIEDioyMLLVdSkqKEhISfJa5XC4lJycHvGcAABC8wuwObU7r71dt85EzAtwNAAS/kAyT+/bt08MPP6z09HTFxMRIkpo0aaLdu3drz549ql27tpYuXaru3buX2tbpdMrpdFZ0ywAAAABgKSEZJmfOnKn8/HxNmTLFu6xnz56aMmWKhgwZovz8fLVp00YdO3Y0sUsAAAAAsK6QDJOpqalKTU0tc93ixYsruBsAAAAAuPCE5K1BAACAOUqKCs1uAQAQJEJyZhIAAJiDi9QAAE5jZhIAAAAAYBhhEgAAAABgmGmHuY4ZM+aM62w2m55++ukK7AYAAAAAYIRpYbJ+/fqllh09elSZmZmqVauWCR0BAAAAAPxlWpi8//77fR5nZ2dr1KhRiouLO+NtPwAAAAAAwcH0q7kWFRXp+eefV1ZWliZMmKCOHTua3RIAAAAA4BxMDZN79uzRiBEjVLVqVWVlZalmzZpmtgMAAAAA8JNpV3OdP3++evTooQ4dOujNN98kSAIAAABACDFtZjI1NVVhYWF65ZVX9Oqrr3qXezwe2Ww2bdmyxazWAAAAAADnYFqYXLNmjVlPDQAAcEEpKSpUmN1hdhsALMa0MFmrVi15PB4VFxfLbrcrLy9P2dnZuv7661W3bl2z2gIAALCcMLtDm9P6+13ffOSMAHYDwCpMO2fyu+++U/v27fXRRx/p5MmT6tGjh1544QXdf//9+vjjj81qCwAAAADgB9PCZFpamoYPH662bdtq2bJl8ng8Wrp0qebMmaNp06aZ1RYAAAAAwA+mhcl9+/apW7dukqRPP/1Ud955p8LCwlSzZk3l5eWZ1RYAAAAAwA+mhcmwsP899eeff65bbrnF+zg/P9+MlgCEmJKiwoDUIrgUGHjvjNQCAIA/xrQL8FxyySX6+uuvlZeXp4MHD3rD5JYtWxQVFWVWWwBCiJELSnAxidAVYXeo76xhftW+3m9qgLsBAACnmRYm//a3v6lv377Ky8vTo48+qqpVq2rmzJl6+eWX9dJLL/m1j7y8PPXs2VMvv/yyateurTFjxmjz5s2qUqWKJGnw4MHq0KFDIIcBAAAAABck08Jk3bp1tWzZMtlsNoWFhenYsWNq0qSJXnvtNdWpU+ec22/dulWpqanKycnxLtu+fbvefPNNRUZGBrBzAAAAAIBpYbJVq1ay2Wzexx6Px/uzzWbTjh07zrr93LlzNX78eI0cOVKSdPz4cf3000964okn9NNPP6lDhw4aPHiwz7mZkuR2u+V2u32WuVyuPzocAAAAALigmBYmExIStGXLFrVr107du3fXddddZ2j7SZMm+Tw+fPiwWrVqpYkTJ6pq1ap68MEHNX/+fN1zzz0+dZmZmcrIyPjD/QMAzl9JUaHC7A6z2wAAAH+AaWFy8uTJOnHihFauXKlJkybp+PHj6tatm+Li4uR0Og3vr06dOj7nWt53331auHBhqTCZkpKihIQEn2Uul0vJycnnNxAAgGFcPAkAgNBnWpiUpCpVqig+Pl7x8fFyuVxatGiR+vTpo7p16+qFF14wtK+dO3cqJydHd911l6RTh83a7aWH53Q6zyusAgAAAAD+x7T7TP7ekSNHdOTIER09elS//PKL4e09Ho+efvpp/fzzzyosLNS7777LlVwBAAAAIEBMnZnct2+fFi9erMWLFyssLEzdunXT3Llzz+s+kw0bNtTAgQPVq1cvFRUVKTY2Vl27dg1A1wAAAAAA08Lkfffdp927d6tz58569tln9ac//em89rN27Vrvz8nJyZz7CAAAAAAVwLQw+dlnn6lSpUqaN2+e5s+f713u8Xhks9m0ZcsWs1oDAABACOEK0YA5TAuTa9asMeupAQAAYCFcIRowh2lhslatWmY9NQAAAADgDwqaq7kCAAAAAEIHYRIAAAAAYBhhEsAFoaCoMCC1wB/B5xIAEMpMvc8kAFSUCLtDfWcN86v29X5TA9wNcAqfSwBAKGNmEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhErC4EgMX7TBSCwAAgAsbF+ABLC7M7tDmtP5+1TYfOSPA3QAAAMAqmJkEAAAAABhGmAQAAAAAGEaYBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYFtJhMi8vT127dlVubq4kKTs7W3FxcYqNjVV6errJ3QEAAACAdYVsmNy6dat69eqlnJwcSdLJkyc1duxYTZ8+XcuXL9f27du1bt06c5sEAAAAAIsK2TA5d+5cjR8/XpGRkZKkbdu26eqrr1adOnVkt9sVFxenFStWlNrO7XYrNzfX54/L5aro9gEAAAAgpNnNbuB8TZo0yefxgQMHVKNGDe/jyMhI7d+/v9R2mZmZysjICHh/AFCRCooKFWF3lHstAADAmYRsmPw9j8dTapnNZiu1LCUlRQkJCT7LXC6XkpOTA9YbAARahN2hvrOG+VX7er+pAe4GAABcCCwTJqOionTo0CHv4wMHDngPgf0tp9Mpp9NZka0BAAAAgOWE7DmTv9ekSRPt3r1be/bsUXFxsZYuXarWrVub3RYAAAAAWJJlZiYrVaqkKVOmaMiQIcrPz1ebNm3UsWNHs9sCAAAAAEsK+TC5du1a788xMTFavHixid0AAAAAwIXBMoe5AgBgZcUFhQGtBwDAqJCfmQQA4EIQHuHQ8j79/K7v/MasAHYDAAAzkwAAAACA80CYBAAAAAAYRpgEAAAAABhGmASAC4yRC7NwEZcLQ0FhsdktWBq/cwCsigvwAMAFxsiFXLiIy4UhwhGu3iPn+FX7VlpygLuxHn7nAFgVM5MAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJNACArUxTIKivy/8IORWkM9cCEQr5IAvcYAAADlgQvwACEoUBfLiLA71HfWML9qX+831e/9GsGFQP4nzO7Q5rT+ftU2HzkjwN0AAAD4YmYSAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAfqe4wP+rqBqpBVDxrHyFaCuPDUBosNzVXPv06aPDhw/Lbj81tIkTJ6pJkyYmdwUglIRHOLS8Tz+/aju/MSvA3QD4I6x8hWgrjw1AaLBUmPR4PNq1a5c+/PBDb5gEAAAAAJQ/Sx3mumvXLtlsNg0YMEDdunXTm2++aXZLAAAAAGBJlpq+c7vdiomJ0YQJE3Ty5En16dNH9erV02233eZT43a7fbZzuVwV3SoAAAAAhDRLhcmmTZuqadOmkqSqVasqKSlJ69at8wmTmZmZysjIMKvFkFVSVKgwu6Pca4OB0X5DbXwAEAqKCwoVHuHfd6uRWuCP4HMJnJ2lwuSmTZtUWFiomJgYSafOofz9uZMpKSlKSEjwWeZyuZSczInpZxNmd2hzWn+/apuPnBHgbsqXkbFJoTc+AAgFXPgKwYjPJXB2ljpn8pdfflFaWpry8/OVl5enrKwsdejQwafG6XSqdu3aPn+io6NN6hgAAAAAQpOlZibbtm2rrVu36u6771ZJSYl69+7tPewVAAAAAFB+LBUmJWn48OEaPny42W0AAAAAgKVZ6jBXAMCFrbigMCC1BYXF59MOcEEI1O8dgOBnuZlJAMCFK1AXy4hwhKv3yDl+1b6VxgXdcGHhIjXAhYuZSQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJWBJXXgQqHr93AABcWLiaKyyJKy8CFY/fOwAALizMTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTF7AuFgGUPH4vQMAAFbBBXguYFwsA6h4/N4BAACrYGYSAAAAAGAYYRIAAAAAYBhhEgAAAABgGGHyHIxcLIMLa1wYCooK/a4tLigwUOv/foOBkX5DbWwAAPPx7yog+HEBnnPgYhn4vQi7Q31nDfOr9vV+U7W8Tz+/aju/MeuPtFXhwiMclh0bAMB8/BsMCH6Wm5lcsmSJOnfurA4dOmjOHP++gAAAAAAAxlhqZnL//v1KT0/XggULFBERoZ49e6ply5a67rrrzG4NAAAAACzFUjOT2dnZatWqlapXr66qVavqrrvu0ooVK8xuCwAAAAAsx1IzkwcOHFCNGjW8jyMjI7Vt2zafGrfbLbfb7bPsxx9/lCS5XK4y95t//Jhfz5+bm2ug2+BgZGwHfznpd20wCMTYTtefPHbc79oj+YF53QL13jG2wI5Nsvb4GFtwjO10vRGhND7G9j/8Xfe//QbzexcdHS273VL/7AYkSTaPx+Mxu4ny8vLLL+vEiRMaMWKEJGnevHn64osvNHHiRG/NtGnTlJGRYVaLAAAAuMCsWbNGtWvXNrsNoNxZ6r9IoqKitGnTJu/jAwcOKDIy0qcmJSVFCQkJPssKCgq0d+9e1a1bV+Hh4QHv0+VyKTk5WXPmzFF0dHTAn68iWXlskrXHx9hCl5XHx9hCl5XHZ+WxSdYen1ljs9rrCJxmqTB56623atq0aTpy5IiqVKmilStX6sknn/SpcTqdcjqdpba95pprKqpNr+joaMv+L5WVxyZZe3yMLXRZeXyMLXRZeXxWHptk7fFZeWxARbJUmIyKitKIESPUp08fFRYWKikpSY0bNza7LQAAAACwHEuFSUmKi4tTXFyc2W0AAAAAgKVZ6tYgAAAAAICKQZg0gdPp1ODBg8s8dzPUWXlskrXHx9hCl5XHx9hCl5XHZ+WxSdYen5XHBpjBUrcGAQAAAABUDGYmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYlFRUVKTc3V0VFRWa3AgAAAAAhgTApyeVyqX379nK5XGa3AgAAAAAhgTAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAQDkqKSoMSC0AAMHGbnYDAABYSZjdoc1p/f2qbT5yRoC7AQAgcJiZBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYFtJhMi8vT127dlVubq4kKTs7W3FxcYqNjVV6errJ3QEAAACAdYVsmNy6dat69eqlnJwcSdLJkyc1duxYTZ8+XcuXL9f27du1bt06c5sEAAAAAIsK2TA5d+5cjR8/XpGRkZKkbdu26eqrr1adOnVkt9sVFxenFStWlNrO7XYrNzfX54/L5aro9gEAAAAgpNnNbuB8TZo0yefxgQMHVKNGDe/jyMhI7d+/v9R2mZmZysjICHh/AAAAAGBlIRsmf8/j8ZRaZrPZSi1LSUlRQkKCzzKXy6Xk5OSA9QYAAAAAVmOZMBkVFaVDhw55Hx84cMB7COxvOZ1OOZ3OimwNAAAAACwnZM+Z/L0mTZpo9+7d2rNnj4qLi7V06VK1bt3a7LYAAAAAwJIsMzNZqVIlTZkyRUOGDFF+fr7atGmjjh07mt0WAAAAAFhSyIfJtWvXen+OiYnR4sWLTewGAAAAAC4MljnMFQAAAABQcQiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAABZXUlQY0HoAFya72Q0AAAAgsMLsDm1O6+93ffORMwLYDQCrYGYSAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGCY5cLkokWL1KVLF3Xp0kXPPPOM2e0AAAAAgCVZKkyeOHFCkyZN0uzZs7Vo0SJt2rRJ2dnZZrcFAAAAAJZjN7uB8lRcXKySkhKdOHFCVatWVVFRkSpVquRT43a75Xa7fZa5XK6KbBMAAAAAQp6lwmS1atU0bNgwderUSZUrV1aLFi3UrFkzn5rMzExlZGSY1CEA+K+kqFBhdke51wYDK48NAIALhaXC5Ndff6333ntP//rXv3TxxRfr0Ucf1cyZM9W/f39vTUpKihISEny2c7lcSk5Oruh2AeCswuwObU7rf+5CSc1HzghwN+XLymMDAOBCYalzJtevX6+YmBhdfvnlioiIUGJiojZu3OhT43Q6Vbt2bZ8/0dHRJnUMAAAAAKHJUmGyYcOGys7O1vHjx+XxeLR27Vo1atTI7LYAAAAAwHIsdZjr7bffrq+++kqJiYlyOBxq1KiRBg4caHZbAAAAAGA5lgqTkjRw4EACJAAAAAAEmKUOcwUAAAAAVAzCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAPBbSVFhQGoBAKHHbnYDAAAgdITZHdqc1t+v2uYjZwS4GwCAmZiZBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYZnqYPHHihP7zn/9IkjIzMzVmzBj99NNP5jYFAAAAADgr08PkmDFjtGbNGm3btk1vvPGGrrzySj3xxBNmtwUAAAAAOAvTw+TevXv1yCOP6F//+pcSEhI0ZMgQHTt2zOy2AAAAAABn4VeY7NGjh2bOnKm9e/ees7akpKTUsrNtV1h46obG69evV6tWrVRcXKzjx4/70xYAAAAAwCR+hckxY8boyJEjGjBggBISEvTyyy9r165dZdaOGjXK5/G8efPUvXv3M+67WbNm6ty5s06ePKlmzZqpb9++uvXWWw0MAQAAAABQ0ez+FDVr1kzNmjXTY489puXLl+vZZ5/V1KlTtWPHjlK1hYWFeuqppzRo0CA9/vjj2rt3r2bMmHHGfT/xxBP6/PPP1aBBA4WFhemBBx5Q69atz39EAAAAAICA82tmcsGCBXr00UfVunVrvfHGG0pISNCcOXPKrH3++eflcrl055136pprrtGCBQvUuHHjM+47PDxchw4d0owZM3TixAnl5eUpLOz8T+Vcu3atEhMT1bFjRz311FPnvR8AAAAAwJn5NTM5adIkVa1aVQ8++KA6duyoK664olTNypUrvT937NhR//nPf2Sz2fThhx9KkmJjY8vc9yuvvKKPP/5YLpdLffv2VUZGhvbs2aOHH37Y8GD27t2r8ePHa968ebr88suVkpKidevWqU2bNob3BQAAAAA4M7/C5KeffqotW7boo48+0sCBA1VSUqKYmBif8yNnz57ts029evW0detWbd26VTab7YxhctmyZZo3b57uueceXXrppZo7d67uvffe8wqTq1atUufOnRUdHS1JSk9PV6VKlQzvBwAAAABwdn6FSbvdrhYtWqhKlSqqVKmSli5dqg8//LDMMDlnzhwlJyf734DdroiICO9jp9Mpu92vtkrZs2ePHA6HHnjgAR08eFBt27bV8OHDfWrcbrfcbrfPMpfLdV7PBwAAAAAXKr9S22OPPaYNGzYoKipKsbGxeumll3TttdeWWfv2228bCpM1a9bUhx9+KJvNpoKCAs2cOVO1atXye/vfKi4u1qZNmzR79mxVrVpVgwYNUlZWlhITE701mZmZysjIOK/9AwAA6yopKlSY3VHutQg83jvAHH6FyUaNGmnEiBG68sorz1lbr149paam6uabb1bVqlW9y890mOsTTzyhkSNHaufOnbrpppvUpEkTPffcc3627+uKK65QTEyMLrvsMklS+/bttW3bNp8wmZKSooSEBJ/tXC6XoQAMAACsJ8zu0Oa0/n7VNh955ivVo+Lx3gHm8CtMJiYm6vnnn9euXbs0depU/f3vf9eoUaN00UUXlao9duyYjh07pj179niXne2cyaioKGVmZurEiRMqLi5WtWrVznMoUtu2bTVq1Ci53W5ddNFF+uijj9S+fXufGqfTKafTed7PAQAAAAAwcDXXyMhIHT58WJUqVVJeXp7GjRun559/vlTt6XMni4qK5PF45HCc/TCCgwcPKisrS8eOHfNZPnLkSD+H8D9NmjRR//791bt3bxUWFuq2225T9+7dDe8HAAAAAHB2foXJHTt2aPLkyVq3bp2qVKmi5557Tl27di2z9vDhwxo1apQ++eQTFRcX65ZbbtGzzz6rqKioMuv/+te/Kjo6WnXq1Dn/UfxGUlKSkpKSymVfAAAAAICy+RUmw8LCfB4XFxeXWnbaxIkTddNNN+nvf/+7iouLNXv2bE2YMEH/+Mc/yqwvLCzkgjgAAAAAEGLKToS/c3p28eTJk/roo480ePBgtWjRoszanJwcDR48WE6nU5deeqmGDh2qH3744Yz7vvHGG/XNN9+cX/cAAAAAAFP4NTP56KOP6pVXXtHFF1+sF154QbfffrsefvjhMmuLioqUn5+vSpUqSZJOnDghm812xn03a9ZMd999t2rUqOFzf8k1a9YYGQcAAAAAoAKdNUyOGTPG5/F1110nSTpw4IAmTJigp59+2rtu6tSpGjp0qDp37qy+fft6b8exYMEC3XXXXWd8joyMDD333HO66qqrznsQAAAAAICKddYwWb9+/VLLjh49qszMTNWqVctn+YYNG7Rp0yY9//zzqlmzpv7973+rpKREiYmJZ70gziWXXKLOnTufZ/sAAAAAADOcNUzef//9Po+zs7M1atQoxcXFKTU11WfdW2+9pZdffllJSUmaNGmSXnjhBb8a+L//+z8988wzio2NVUREhHf5jTfe6OcQAAAAAAAVza9zJouKivT8888rKytLEyZMUMeOHUvVhIWFadCgQfq///s/paamau3atT6Hrvbr16/MfS9ZskSS9MEHH3iX2Ww2zpkEAAAAgCB2zjC5Z88ejRgxQlWrVlVWVpZq1qx51vqwsDDZbDZ99913Onny5DkbWLt2rf/dAgAAAACCwlnD5Pz585WWlqZ+/frpr3/961l35PF49PLLL2vmzJkaMWKEkpOTz1r/yiuvaODAgXrqqafKXP/7w2gBAAAAAMHjrGEyNTVVYWFheuWVV/Tqq696l3s8HtlsNm3ZssW7rGfPnjp+/LjeeustXX/99ed84pdeekkDBw5U9erVz797AAAAAIApzhomjZy3+Oc//1mjRo3yuYjO2dSrV0+SNHjwYL+fAwB+q6SoUGF2R7nXAsGouKBQ4RH+f4aN1gMAYNRZw+Tvb/9xNk888YShJ87Pz9dXX30lj8dT5nqu5grgXMLsDm1O6+9XbfORMwLcDRBY4REOLe9T9sXsytL5jVkB7AYAAD+v5hoIe/fu1ZAhQ8oMk1zNFQAAAACCm2lh8rrrrtPChQvNenoAAAAAwB9gWpgEAAAALkQlJSXKzc3Vr7/+anYrwFlddNFFql27tsLCwspcb1qYvPnmm816agAAAMA0hw4dks1mU4MGDc74j3TAbCUlJfrxxx916NAhRUZGlllj2qeX+0gCAADgQnTs2DFFRUURJBHUwsLCFBUVpZ9//vnMNRXYDwAAAHDBKy4ulsPBrXsQ/BwOh4qKis64njAJAAAAVDCbzWZ2C8A5netzSpgEAAAATFRQWBwU+12wYIFGjx4dkF7OZdq0aZo2bZrf9evWrVPbtm31yCOP+CwfPXq0GjZsqP379/ssHzRokNq1a2eop/vuu0+ffvqpvvjiCz3++OOGtpWk3NzcUs+5Z88e3XzzzcrPz/dZnpWVpcGDB+vtt9/W22+//YeeoyJxNVcAAADARBGOcPUeOafc9/tWWnK57zNYrFixQg899JDuvffeUuuioqK0cuVK3XfffZKkvLw8ffXVV+d9jmqjRo3UqFGjP9TvaVdffbUaNGigDz/8UHfddZd3+cKFC9W3b1+1bdu2XJ6nolh2ZvKZZ54x7X9WAAAAgFC2e/du3XfffYqLi9O9996rbdu2STo187dgwQJvXYMGDSSdmll84IEH1LlzZ82ZM0f33Xef0tLSdO+996pDhw5at26dJOmbb77Rfffdp+7du6tt27Z64403ztrHv/71L8XHxysuLk6DBg3SoUOHNG/ePK1Zs0b/+Mc/NG/evFLbxMbG6oMPPvA+Xr16tf7v//7P+/jXX3/VqFGjlJiYqPj4eC1dulSSVFBQoMcee0ydOnVS//79dfToUUnSp59+6g2mO3bsUI8ePRQXF6e//OUvcrlcKioqUmpqqu699161b99e/fv318mTJ884pu7du3ufU5JcLpdycnLUunVrnxnaVq1a6YEHHlB8fLwKCwv18ssvq3PnzoqLi9OUKVNUXOw783zo0CE9+OCDiouLU0JCgv79739Lkn755Rf99a9/VZcuXfTQQw/p7rvvVm5urnr37q3169dLkjwej2JjY0vN6J6LJcPkhg0blJWVZXYbAAAAQEh67LHHdN9992nJkiUaM2aMhg0bpoKCgrNuU1BQoOXLlys5+dSMaGFhod59912NGTNGU6dOlSTNmzdPgwYN0nvvvac33nhD6enpZ9zf4cOHNW7cOL300ktasmSJmjVrpokTJ6pHjx5q166dhg4dqh49epTa7oYbbtDhw4d16NAhSdL777+vTp06edf/4x//0I033qgFCxZozpw5evnll7V3717Nnj3bW5+amqoffvih1L4fffRRDRo0SEuWLFHnzp2VmZmpzz//XA6HQ++++65WrVql/Px8b3guS8eOHfXZZ58pLy9PkrR48WJ169ZN4eHhPnVHjx7VwIEDtWjRImVnZ2vt2rVasGCBsrKytGfPHr3zzjs+9U8++aRatWqlJUuW6MUXX9TYsWN16NAhvfTSS6pXr56WLVumhx9+WDt37pR0KtQuXrxYkrRp0yZdddVVioqKOmPfZbHcYa7Hjh1Tenq6HnroIX399ddmtwMAAACElF9//VU//PCDYmNjJUk33XSTLrnkEu3ateus2zVu3Njn8R133CFJql+/vo4dOybp1MzmRx99pH/+85/auXOnjh8/fsb9bdu2TY0bN1bt2rUlSffee69eeeUVv8YQGxurlStXqkuXLsrLy1OtWrW867Kzs3Xy5Em99957kqTjx4/r22+/1caNG72HzdatW1dNmzb12eeRI0d08OBB76GovXv39q6rXr265syZo127diknJ+es46pataratWunDz74wBvoMjIyyqxt0qSJJOmTTz5Rly5dVLlyZUmnguDChQvVpk0bb+0nn3yip556SpJUp04dNWnSRFu3btXHH3+s5557TtKpQ3ZPzyZ36tRJ6enpOnHihLKyspSYmHiul7UUy4XJcePGacSIEdq3b1+Z691ut9xut88yl8tVEa0BAAAAQWXTpk2qU6eOoqKi5PF4FB4eLo/HI4/H41Pn8XhUXFwsm83mXVdYWOhTczronFapUiVJvlcEHT58uJxOp9q2bavOnTtr2bJlZ+ytpKSkVA9nu03Fb3Xq1EmTJ09WRESEOnToUGq/zz77rG688UZJpw4PveSSSzR37lyf57TbfaPS72/nkp+frwMHDuibb77Riy++qD59+igxMVFHjx4t9fr9XmJiol566SU1bNhQl1xyierWrVtm3enX9PevhaRSr8WZ3rPT7+nvVa1aVa1bt9aKFSv0ySefaMKECWftuSyWOsx13rx5qlmzpmJiYs5Yk5mZqfbt2/v8OT0VD8C6CooKz110HrUILsUF/r93RmoBwKree+89rV69WpK0c+dO1alTR9WqVVOdOnW0cuVKSdJ//vMfHTp0SPXr11f16tX13XffSZJ3OyM+/vhjDR06VHfeeac+++wzSSp17t9pp2fWcnNzJUnvvvuuWrZs6dfzNGzY0Ht+ZceOHX3WtWrVynvF1AMHDqhbt27at2+fYmJitHTpUpWUlOjHH3/Uli1bfLa7+OKLFR0drY8//liStGjRIk2dOlUbNmxQp06d1L17d11xxRX67LPPzjim026++Wbt379fb731lpKSks45nlatWmnZsmU6efKkioqK9N5776lVq1alaubPny9J2rt3r7Zs2aKbbrpJt956q5YsWSLp1Hv87bffegN+9+7dlZ6erjvuuEMRERHn7OP3LDUzuXz5ch08eFDx8fH6+eefdfz4cT399NMaO3astyYlJUUJCQk+27lcLgIlYHERdof6zhrmV+3r/aYGuBsESniEQ8v79POrtvMbswLcDQAEv4EDB2rkyJF68803FR0drRdeeEGS9Oyzz2rChAmaNm2aHA6Hpk2bpoiICPXu3VvDhw9XXFycWrVqpRo1ahh6viFDhqh3795yOp2qV6+eatWq5Q2Lv3fFFVdo4sSJGjx4sAoLC3XllVdq0qRJfj9Xhw4dtHHjRkVHR/s8x+DBgzVhwgR17dpVxcXFeuyxx3TVVVepd+/e+vbbb9WpUyfVqlVL119/fal9nn5d0tLSdOmllyotLU1Hjx7Vo48+qhUrVigiIkI33XTTGcf0W/Hx8Xr11VeVmpp6ztq2bdtqx44d6t69u4qKinTHHXd4LwB02uOPP65x48Z5L5D01FNPKTIyUoMGDdKYMWMUFxenq666SldccYV3xrN58+ay2Wzq3r37OXsoi81zrjnYELVgwQJt3LhRU6ZMOWdtbm6u2rdvrzVr1niPyQYQGjan9ferrvnIGSEZJo2ML9QEamzBECbNHpsUmuMLBoztFCuPLxjGtmPHDt1www3exwWFxYpwhJ9li/MTqP0itCxatEi1a9dW8+bN9dNPP+kvf/mLVq9eLZvNpm+++UajRo3SwoULz7j97z+vv2WpmUkAAAAg1AQq8BEkIUnXXHONxo8fr5KSEoWFhWnixIkKCwvT66+/rhkzZnivtHs+LBsmExMTz+uKRAAAAABgFY0aNfK5N+hpffv2Vd++ff/Qvi11AR4AAAAAQMUgTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAACYqKSoMqf2eyaeffqr77rtPkjR69Gj93//9n+Lj4xUfH6/Y2Fjdc889+v7778+4/RdffKHHH3+8otpFObDsrUEAAACAUBBmd2hzWv9y32/zkTPKfZ9GDB061OdWfZMmTdK0adP0wgsvlFnfqFEjNWrUqIK6Q3lgZhIAAAC4gH366afq16+f+vbtq3bt2umZZ57R9OnTvfdtP3TokP79738rKSlJd999twYPHqyjR49KktavX68uXbooMTFRc+fOPeNzFBQU6ODBg7rkkkuUl5endu3aacOGDZKkBx54QHPmzPHObH799dfq2rWrd9t//etfeuihhwL7IuC8ECYBAACAC9zWrVs1efJkLVu2TO+8844uu+wyLViwQA0aNNA777yj559/XjNnztTChQt1++2367nnnlNBQYFGjx6tF198UQsWLFDlypV99vniiy+qW7duat26tbp06aKaNWvqscceU7Vq1TRp0iRNmDBBc+bMkc1mU3Jysne7hg0bKiwsTN98840kaenSperWrVuFvh7wD4e5AgAAABe466+/XjVr1pQkXXrppYqJiZEkXXnllVq7dq327dunPn36SJJKSkp0ySWXaOfOnYqMjNS1114rSUpISNDUqVO9+zx9mOuuXbt0//33q2XLlqpWrZokKSYmRq1atdLf//53vf/++6X6iY+P17Jly1SnTh1t3LhRTz/9dEDHj/NDmMQFr6SoUGF2R8DqzWak31AbG85PcUGhwiP8e5+N1AK4MPGdYg0Oh+/7Eh4e7v25pKREzZo108svvyxJys/P16+//qqffvpJJSUlZW7zW9dcc40effRRjR07Vh988IEuvvhieTwe7d69W1WqVFFOTo4iIyN9tunatatSUlLUsGFD3X777apUqVJ5DRXliDCJC57Rk97NPpndKCPjC7Wx4fyERzi0vE8/v2o7vzErwN0ACHV8p1hf48aNtXLlSu3evVv16tXT9OnTtX//fj355JM6fPiwvv76azVs2FDLli074z66du2q2bNna/r06Ro1apTeeustVa1aVdOnT9ejjz6qhQsX+tRHRUWpZs2aeuWVVzRy5MgAjxDnizAJAAAAmKikqDAg/6FbXkcc1ahRQ08//bSGDx+ukpISRUVF6dlnn5XD4dDf//53PfbYY7Lb7frTn/501v2MHDlSffv2Va9evfSPf/xD8+bNU82aNXX77bcrLS1NnTp18qmPj49Xenq6WrZs+YfHgMAgTAIAAAAmCtQpJv7ut2XLlj6Bbe3atd6fhwwZ4v25Xbt2pba95ZZbtGTJklLLp0yZUmpZ8+bN9cUXX0g6dRXY08aNG+fTy2l333237r77br/GAHNwNVcAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYYRJAAAAAIBhhEkAAADARAVFhabt99NPP1XTpk0VHx+vbt26qVOnTvrHP/5RZu3+/fs1YMAASaduHzJr1ixJ0ttvv6233377jM8xevRoLViw4Kx9LFiwQA0aNNDSpUt9lr/++utq0KCBcnNzzzmW06ZNm6Zp06ZJOnWvyvPRrl27sz7nb5+jovnzelYU7jMJAAAAmCjC7lDfWcPKfb+v95vqV92f//xnzZ49W5L066+/qnPnzurQoYOuu+46n7qoqCi9+uqrkqQvv/zSu7xXr17l0m90dLQ++OADde3a1bts1apVcjqd573PRYsWlUdrOAPLzUxmZGSoS5cu6tKli9LS0sxuBwAAAAgZJ0+eVHh4uC6++GJJp2bohg8frrvuukvbtm1Tu3bt9N133+mdd97RO++8o/fee887S1dYWKjHHntMd999t+6++27NnTvXu98PP/xQSUlJatu2rd59990yn/uWW27R9u3bdfz4cUnSjz/+qIsuusjbiyS98sorSkhIULdu3ZSWliaPxyNJmjFjhmJjY3Xvvfdq27Zt3voGDRpIko4dO6aHH35YnTp1Unx8vDZs2CBJevPNN9WjRw917dpVcXFx+v777w2/Zv/5z3/Uo0cPdevWTSkpKdqzZ48k6b777tOnn34qScrNzVW7du0knZpZfOihh9SpUyetXbtW7dq10wsvvKCkpCR16dJF27dvlyRt3LhRvXr1UkJCgtq1a6f333/fcG+BZqkwmZ2drfXr1ysrK0sLFy7Ul19+qVWrVpndFgAAABC0tm/frvj4eMXFxaldu3Zq0aKFIiMjvetbt26tDz74QJdddpkk6brrrlPPnj3Vs2dPde/e3Vv3+eef6+eff9bChQs1a9YsbdmyxbuuoKBA8+bN0z//+U+lp6eX2Yfdbtftt9+udevWSZLef/99derUybv+3//+t7Zv36758+dr4cKF2r9/vxYvXqwvvvhC7733nrKysjRr1iy5XK5S+546daquuuoqvf/++0pLS9MLL7ygvLw8rV69WrNnz9bSpUt155136q233jL02hUUFOhvf/ubnnjiCS1evFg9e/bU3/72t3NuV716db3//vvegFm9enXNnz9fPXv21D//+U9Jp4LuU089paysLE2aNEnTp0831FtFsFSYrFGjhkaPHq2IiAg5HA5de+21+umnn3xq3G63cnNzff6U9YEDAAAALgR//vOftWjRIi1ZskTZ2dn68ccf9corr3jXN2nSxK/91K9fX7t379YDDzygxYsX69FHH/Wua9++vWw2m+rXr6+jR4+ecR+dOnXSBx98IElavXq17rzzTu+6DRs2aNu2bUpMTFRCQoK2b9+u7777Ths3blSbNm100UUXqWrVqurYsWOp/X722Wfe8ycbNGigd999V9WqVdPzzz+vZcuW6fnnn9e//vUv76yov3JycuR0OtW4cWNv/z/88IN++eWXs253uv60O+64Q9Kp1/DYsWOSpGeffVbffvutXnrpJc2aNUu//vqrod4qgqXOmaxfv77355ycHC1fvlzvvPOOT01mZqYyMjIqujVUsILCYkU4ws1uw9KKCwoVHuEo99pgeO8CNTacn4KiQkXYrfkaW3lsRoXad4qR9473ObgYeT+KCwoUHhHhZ601/j646KKLdOeddyo7O9u7rFKlSn5te+mll2rZsmX6+OOPtW7dOiUkJGjZsmWSpPDwU7+HNpvtrPto2bKlUlNT9c033+jSSy/1OcS1uLhYKSkp6tevn6RTk0Th4eF69913VVJS4q2z2+0qKCjw2a/d7ht7vv/+e1WuXFkpKSn6y1/+otatW+uKK67Qjh07ztjbunXr1Lx5c1WrVk0ej0d2u93neU/zeDwqLi72/ixJRUVFPjWVK1f2eXz6Nf7t69O7d2+1bNlSLVu2VExMjE84DxaWCpOnffvtt3rwwQc1atQo1a1b12ddSkqKEhISfJa5XC4lJydXYIcItAhHuHqPnONX7VtpvPfnIzzCoeV9+vlV2/mNWX7vNxjeu0CNDefHyIUp/L3YRLCw8tiMCrXvFN670GX0vbvQ/j4oLi7Wxo0b9ac//emsdeHh4crPz/dZtmbNGi1atEhTp07VHXfcoQ0bNmjfvn2Gnj88PFy33367xo0bV+rf561atdKLL76oe+65R5UqVdLDDz+shIQExcTEaNiwYRoyZIgiIiK0atUqtWnTxmfbm2++WcuXL1fDhg31/fffa8CAARo1apSuvvpq9e3bVwUFBXr55Ze9h/KWZcaMGZKkNm3aaOfOnerYsaOuueYaHTt2TNu2bVPjxo21fPlyXXnllapevbouvfRSfffdd2rVqpVWr15t6HU4duyYcnJy9NZbb6lSpUqaNm2aN6AGE8uFyc2bN2vo0KEaO3asunTpUmq90+n8Q1eEAgAAAKzk9DmTknTixAk1atTIewuQM7nllls0atQoXXHFFd5lp8+t7NKliypVqqTY2FjvBXCM6NSpkxYtWuQ9n/C0du3a6euvv9Y999yj4uJi3XHHHUpISJDNZlNKSoqSkpLkdDp15ZVXltrn0KFDlZqaqm7duslutystLU033HCD3nnnHXXu3FkRERFq3Lixvv322zP29be//U0TJkzQ008/rYYNG+quu+5SRESE0tPT9eSTT+rEiRO65JJLvOeE9u/fX6NHj9Z7772n9u3bG3oNqlevrh49eqhLly6qVq2abrrpJp08edLwYbiBZqkwuW/fPj388MNKT09XTEyM2e0AAAAA51RQVBiQGWx/Dult2bKlPv/88zOuX7t2rffn2rVrex/fcsstPutOK+tuClOmTPF5vHPnzlI1iYmJSkxMlCTFxMRo69atZfYwaNAgDRo0qNT2ycnJZR5pePq5nE6nXnzxxVLrT98r8/fKGlvTpk3LvNVI06ZNNW/evFLLT89UnjZ48GBJpV+P3z7X6cNapVNXfR09erR33YQJE8rc3kyWCpMzZ85Ufn6+zwvcs2fPcrv3DQAAAFDeAnVOLefqItAsFSZTU1OVmppqdhsAAAAAYHmWujUIAAAAAKBiECYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAATFRcUmrrfTz/9VPfdd1+p5fHx8efcrmnTpoqPj1d8fLzi4uLUrl07vf3222fdbsCAAdq/f79fvSG4WerWIAAAAECoCY9waHmffuW+385vzPpD2y9atOicNX/+8581e/Zs7+MdO3YoKSlJcXFxqlatWpnbvPrqq3+oLwQPZiYBAACAC9zRo0f1wAMPKC4uTo8//rgKCgrUoEEDSdKJEyf0yCOPqGvXroqLi9PChQvPuJ8ff/xRVapUUUREhCZPnqzHHntMkrRkyRLde++9Ki4uVrt27ZSbm6uEhAR98cUXkqTi4mK1bt1ahw8fDvhYUX6YmQQAAAAucLm5ucrIyNDVV1+tESNG+ByqOm3aNF166aVaunSpjhw5oh49eqhhw4aSpO3btys+Pl4nTpzQzz//rJYtW+q1115TRESERowYoYSEBC1dulR///vflZmZqfDwcO9+4+PjtXz5cjVq1EiffPKJGjRooMsvv7zCx47zx8wk/FJS5P+x/EZqrc7IORBGagsKi8+nnXPvl/cu4HjvEIz4XIamQL1vEu/dhejmm29W3bp1ZbPZFBcXp40bN3rXffLJJ0pKSpIkXXbZZWrfvr13/Z///GctWrRIS5cuVatWrVS5cmU1btxYklS5cmVNnjxZjz76qPr376+rrrrK5zm7dOmilStXyuPxaOnSperWrVsFjRblhZlJ+CXM7tDmtP5+1TYfOSPA3YQOI+dAGDmvIcIRrt4j5/hV+1Zasv/7tTvUd9Ywv2pf7zfV7/3if3jvEIz4XIamQL1vEu/dhchu/18s8Hg8pR7/lsfjUXGx739mRERE6KmnntJdd92lpUuXqmvXrpKk3bt367LLLtP27dtLPWeNGjVUt25dffrpp9qwYYPGjRtXnkNCBWBmEgAAALjAbd68WT/99JNKSkq0cOFC3Xrrrd51rVq10vz58yVJR44c0Zo1a9SiRYtS+7j44os1ZMgQPffcczp58qT279+vF154Qe+++6527NihdevWldomPj5ezzzzjFq0aKEqVaoEboAICGYmAQAAABMVFxT+4Suvnmm/4REOv2qvu+46jR07VgcPHlSrVq2UlJTknSl8+OGHNWHCBMXFxam4uFgPPfSQbrzxRn366ael9tOjRw+98cYbeu211/Sf//xH/fr1U506dTRx4kQNHTpUixcv9qnv0KGDxo8fr0ceeeSPDxgVjjAJAAAAmMjfwBeo/bZs2bLMe0Pu3LlTklStWjU999xzZW7XsmVLn2V2u13vv/9+qdrGjRvrww8/lCStXbvWu7xKlSr6/PPP/eoTwYfDXAEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAIAK9vvbbQDB6FyfU8IkAAAAUIEqV66sw4cPEygR1Dwejw4fPqzKlSufsYaruQIAAAAVqHbt2srNzdXBgwfNbgU4q8qVK6t27dpnXE+YBAAAACqQw+FQvXr1zG4D+MMsd5jrkiVL1LlzZ3Xo0EFz5swxux0AAAAAsCRLzUzu379f6enpWrBggSIiItSzZ0+1bNlS1113ndmtAQAAAIClWCpMZmdnq1WrVqpevbok6a677tKKFSs0ePBgb43b7Zbb7fbZ7scff5QkuVyuCus1FB385aRfdbm5uQHuxD/5x4/5VZebm+v32E7XG3EkPzCvWyDGl5ubq5PHjvtdy9jO7/Nu9fH5i7EFbmyn642w8ucyGMYWKIH8uy4Y3jsjgvlzGR0dLbvdUv/sBiRJNo+FLiP1z3/+U8ePH9eIESMkSfPmzdO2bdv05JNPemumTZumjIwMs1oEAADABWbNmjVnvYgJEKos9V8kZeVim83m8zglJUUJCQk+ywoKCrR3717VrVtX4eHhAe1ROjUDmpycrDlz5ig6Ojrgz1eRrDw2ydrjY2yhy8rjY2yhy8rjs/LYJGuPz6yxWe11BE6zVJiMiorSpk2bvI8PHDigyMhInxqn0ymn01lq22uuuSbg/f1edHS0Zf+Xyspjk6w9PsYWuqw8PsYWuqw8PiuPTbL2+Kw8NqAiWepqrrfeeqs2bNigI0eO6MSJE1q5cqVat25tdlsAAAAAYDmWm5kcMWKE+vTpo8LCQiUlJalx48ZmtwUAAAAAlmOpMClJcXFxiouLM7sNAAAAALA0Sx3mGiqcTqcGDx5c5rmboc7KY5OsPT7GFrqsPD7GFrqsPD4rj02y9visPDbADJa6NQgAAAAAoGIwMwkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwKamoqEi5ubkqKioyuxUAAAAACAmESUkul0vt27eXy+UyuxUAAAAACAmESQAAAACAYYRJAAAAAIBhhEkAAAAAgGGESQAAAACAYSEdJvPy8tS1a1fl5uZKkt5991117dpVcXFxGjNmjAoKCkzuEAAAAACsKWTD5NatW9WrVy/l5ORIknbv3q2ZM2fqnXfe0eLFi1VSUqK33nrL3CYBAAAAwKJCNkzOnTtX48ePV2RkpCQpIiJCEyZMULVq1WSz2XT99dfrp59+MrlLAAAAALAmu9kNnK9Jkyb5PK5Vq5Zq1aolSTpy5IjmzJmjyZMnl9rO7XbL7Xb7LOP+kgAAAABgTMiGyTPZv3+/+vfvr+7du6tly5al1mdmZiojI8OEzgAAuLAUFBUqwu4o91oAQHCwVJj8/vvvNWDAAP3lL3/R/fffX2ZNSkqKEhISfJa5XC4lJydXRIsAAFwwIuwO9Z01zK/a1/tNDXA3AIDyZpkwmZeXpwceeEAjRoxQfHz8GeucTqecTmcFdgYAAAAA1hOyF+D5vfnz5+vQoUN67bXXFB8fr/j4eE2dyv9yAgAAAEAghPzM5Nq1ayVJffv2Vd++fc1tBgAAAAAuEJaZmQQAAAAAVBzCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAAAAAMAwwiQAAAAAwDDCJAAAAADAMMIkAACAQQVFhQGpBYBQYje7AQAAgFATYXeo76xhftW+3m9qgLsBAHMwMwkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADAspMNkXl6eunbtqtzcXElSdna24uLiFBsbq/T0dJO7AwAAAADrCtkwuXXrVvXq1Us5OTmSpJMnT2rs2LGaPn26li9fru3bt2vdunXmNgkAAAAAFhWyYXLu3LkaP368IiMjJUnbtm3T1VdfrTp16shutysuLk4rVqwotZ3b7VZubq7PH5fLVdHtAwAAAEBIs5vdwPmaNGmSz+MDBw6oRo0a3seRkZHav39/qe0yMzOVkZER8P4AAAAAwMpCNkz+nsfjKbXMZrOVWpaSkqKEhASfZS6XS8nJyQHrDQAAAACsxjJhMioqSocOHfI+PnDggPcQ2N9yOp1yOp0V2RoAAAAAWE7InjP5e02aNNHu3bu1Z88eFRcXa+nSpWrdurXZbQEAAACAJVlmZrJSpUqaMmWKhgwZovz8fLVp00YdO3Y0uy0AAAAAsKSQD5Nr1671/hwTE6PFixeb2A0AAAAAXBgsc5grAAAAAKDiECYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhlkuTC5atEhdunRRly5d9Mwzz5jdDgAAAABYkqXC5IkTJzRp0iTNnj1bixYt0qZNm5SdnW12WwAAAABgOZYKk8XFxSopKdGJEydUVFSkoqIiVapUyey2AAAAAMBy7GY3UJ6qVaumYcOGqVOnTqpcubJatGihZs2a+dS43W653W6fZS6XqyLbBAAAAICQZ6kw+fXXX+u9997Tv/71L1188cV69NFHNXPmTPXv399bk5mZqYyMDBO7BADglIKiQkXYHeVeC/xRfDYB+MNSYXL9+vWKiYnR5ZdfLklKTEzUW2+95RMmU1JSlJCQ4LOdy+VScnJyhfYKAECE3aG+s4b5Vft6v6kB7gb4Hz6bAPxhepg8ceKEVqxYoZ9//lkej8e7vF+/fob31bBhQz377LM6fvy4qlSporVr16pRo0Y+NU6nU06n8w/3DQAAAAAXMtPD5IgRI3TgwAFdf/31stlsf2hft99+u7766islJibK4XCoUaNGGjhwYDl1CgAAAAA4zfQwuWvXLi1fvlx2e/m0MnDgQAIkAAAAAASY6bcGiY6ONrsFAAAAAIBBps9MXn/99erTp4/uuOMOVa5c2bv8fM6ZBAAAAABUDNPD5K+//qqrr75aP/zwg9mtAAAAAAD8ZHqYnDx5stktAAAAAAAMMj1Mfv7553rllVd0/PhxeTwelZSUKDc3Vx9++KHZrQEAAAAAzsD0C/CkpqaqadOmysvLU1xcnKpVq6bY2Fiz2wIAAAAAnIXpM5M2m00DBw7U0aNHdc0116hbt27q1auX2W0BAAAAAM7C9JnJiy66SJJ01VVX6dtvv1WlSpVUXFxsclcAAAAAgLMxfWayUaNGGj58uIYNG6YHH3xQOTk5Cg8PN7stAAAAAMBZmD4z+fjjj6tv376qV6+exo4dq5KSEj333HNmtwUAAAAAOIuAhUm3233W9d9//70k6auvvpLD4dCXX36pGjVqKD4+XidOnAhUWwAAAACAclDuh7nu2rVLQ4YMkdvt1vz589W3b19lZGTo2muv9al75pln9Morr2jIkCGl9mGz2bRmzZrybg0AAAAAUE7KPUw+9dRTGjt2rJ599llFRUXpL3/5i8aNG6c5c+b41L3yyiuSpLVr15Z3CwAAAACAACv3MHns2DHddtttevbZZyVJycnJmjt3bqm6jIyMs+5n8ODB5d0aAAAAAKCcBORqrvn5+bLZbJKkgwcPqqSkpFTN0aNHJZ06LHb37t268847ZbfbtWbNGjVo0CAQbQEAAAAAykm5h8nevXvrgQce0OHDh/X8889r2bJl6t+/f6m6J554QpLUp08fLViwQJdddpkk6a9//asGDRpU3m0BAAAAAMpRuYfJpKQkXXXVVVq3bp2Kioo0ceJE3X777WesP3jwoDdISpLT6dThw4fLuy0AAAAAQDkKyGGuzZs3V4MGDeTxeCSdOo+yevXqZdZef/31GjNmjOLj4+XxeDR//nw1adIkEG0BAAAAAMpJuYfJOXPm6JlnnlFhYaEkyePxyGazaceOHWXWT5o0SdOmTdOkSZNks9l0xx13lHm7EAAAAABA8Cj3MPnaa6/p3Xff1Q033OBX/cSJE5WWllZuz7927VplZGTo+PHjuv3225Wamlpu+wYAAAAAnBJW3ju85JJL/A6SkvT11197D4f9o/bu3avx48dr+vTpWrJkib766iutW7euXPYNAAAAAPifcpuZPHbsmCTppptu0uuvv66uXbvKbv/f7s90zmSNGjXUpUsXNWnSRBdddJF3+fnMKK5atUqdO3dWdHS0JCk9PV2VKlUyvB8AAAAAwNmVW5hs1aqVbDabd5ZxypQp3nVnO2eyadOmatq0abn0sGfPHjkcDj3wwAM6ePCg2rZtq+HDh/vUuN1uud1un2Uul6tcnh8AAAAALhTlFiYbNmyohQsXGt5u8ODBpZYdP378vHooLi7Wpk2bNHv2bFWtWlWDBg1SVlaWEhMTvTWZmZnKyMg4r/0DAAAAAE4ptzBps9nOa7vVq1frxRdf1PHjx+XxeFRSUqJjx47p888/N7yvK664QjExMd77VrZv317btm3zCZMpKSlKSEjw2c7lcik5Ofm8+gcAAACAC1G5hcmTJ0/qq6++OuPFdG688cYyl6elpWn48OF6++23NWDAAK1evdrn3Ekj2rZtq1GjRsntduuiiy7SRx99pPbt2/vUOJ1OOZ3O89o/AAAAAOCUcguTe/fu1ZAhQ8oMkzabTWvWrClzuypVqqhz587asWOHKlWqpAkTJqh79+7n1UOTJk3Uv39/9e7dW4WFhbrtttvOe18AAAAAgDMrtzB53XXXndc5kxERESooKNBVV12lHTt2qGXLliooKDjvPpKSkpSUlHTe2wMAAAAAzq3cwuT5at++vQYOHKgpU6aoZ8+e2rx58xlvIwIAAAAACA7lFiZvvvnm89ruoYceUrdu3RQdHa3p06frs88+U9euXcurLQAAAABAAJRbmExNTT2v7b788ktJ0tGjRyWdCqUul0uXX355ebUGAAAAAChnph/mOmTIEO/PhYWFOnTokG688UbNnz/fxK4AAAAAAGdjephcu3atz+P//Oc/BEkAAAAACHJhZjfwezfddJP30FcAAAAAQHAyfWbyt8HR4/Hoiy++0MmTJ03sCAAAAABwLqaHydPnTNpsNtlsNl122WWaMGGCuU0BAAAAAM7K1DDpcrn06quv6tprr1V6erry8vIUHh6um266ycy2AAAAAADnYNo5k9u2bVNCQoL3MNf3339fl156qb777jvNmTPHrLYAyykpKgxILVBRCgx8Lo3UArAOI39/FRQVGKjlOwU4G9NmJqdOnar09HS1atVKknTRRRdp8ODB+umnnzR06FDdf//9ZrUGWEqY3aHNaf39qm0+ckaAuwGMi7A71HfWML9qX+83NcDdAAhGRv+u4zsFKB+mzUzu3bvXGySlUxffkaQrr7xS+fn5ZrUFAAAAAPCDaWHS4XD4PP7toa2/XwcAAAAACC6mhcmLLrpILpfL57Ek7du3T1WqVDGrLQAAAACAH0wLkz169NAjjzyiw4cPe5f9/PPPGjNmjHr16mVWWwAAAAAAP5h2AZ4ePXpo7969at++va699lrZbDbt2rVLffr0UdeuXc1qCwAAAADgB1PvM/m3v/1NKSkp+vzzzyVJjRs3VmRkpJktAQAAAAD8YGqYlKTLL79cd955p9ltAAAAAAAMMO2cSQAAAABA6LJsmHzmmWc0evRos9sAAAAAAEuyZJjcsGGDsrKyzG4DAAAAACzL9HMmy9uxY8eUnp6uhx56SF9//XWp9W63W26322fZb+93CQAAAAA4N8uFyXHjxmnEiBHat29fmeszMzOVkZFRwV0BMFtBUaEi7I5yrwUAALhQWSpMzps3TzVr1lRMTIwWLFhQZk1KSooSEhJ8lrlcLiUnJ1dEiwBMEmF3qO+sYX7Vvt5vaoC7AQAACH2WCpPLly/XwYMHFR8fr59//lnHjx/X008/rbFjx3prnE6nnE6niV0CAAAAQOizVJicNWuW9+cFCxZo48aNPkESAAAAAFA+LHk1VwAAAABAYFlqZvK3EhMTlZiYaHYbAAAAAGBJzEwCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAghZJUWFZrcAXHD4vQtNvG8AAsFudgMAcL7C7A5tTuvvV23zkTMC3A1wYeD3LjQZed8k3jsA/mFmEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYBhhEgAAAABgGGESAAAAAGAYYRIAAAAAYJjd7AbKW0ZGht5//31JUps2bTRy5EiTOwIAAAAA67HUzGR2drbWr1+vrKwsLVy4UF9++aVWrVpldlsAAAAAYDmWmpmsUaOGRo8erYiICEnStddeq59++smnxu12y+12+yxzuVwV1iMAAAAAWIGlwmT9+vW9P+fk5Gj58uV65513fGoyMzOVkZFR0a2FvJKiQoXZHeVeGwyM9hsM4ysoLFaEI7z891tUqAg/x2akNtQUFxQqPMK/sRmpNcrIZ62gqEAR9gg/a6373gHBKFi+UwLF6uMDcGaWCpOnffvtt3rwwQc1atQo1a1b12ddSkqKEhISfJa5XC4lJydXYIehJ8zu0Oa0/n7VNh85I8DdlC8jY5OCY3wRjnD1HjnHr9q30vz/bEfYHeo7a5hfta/3m+r3fkNNeIRDy/v086u28xuzAtaH0d873jsgOAXLd0qgWH18AM7McmFy8+bNGjp0qMaOHasuXbqUWu90OuV0Ok3oDAAAAACsw1Jhct++fXr44YeVnp6umJgYs9sBAAAAAMuyVJicOXOm8vPzNWXKFO+ynj17qlevXiZ2BQAAAADWY6kwmZqaqtTUVLPbAAAAAADLs9R9JgEAAAAAFYMwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDB5ASsoLDa7hZBUUFQYkFqgohQX+P+5NFKLwDL6XvDeAX8c35fA2dnNbgDmiXCEq/fIOX7VvpWWHOBuQkeE3aG+s4b5Vft6v6kB7gYwLjzCoeV9+vlV2/mNWQHuBv4y8r5JvHdAeeD7Ejg7ZiYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhhEmAQAAAACGESYBAAAAAIYRJgEAAAAAhlkuTC5ZskSdO3dWhw4dNGfOHLPbAQAAAABLspvdQHnav3+/0tPTtWDBAkVERKhnz55q2bKlrrvuOrNbAwAAAABLsdTMZHZ2tlq1aqXq1auratWquuuuu7RixQqz2wIAAAAAy7HUzOSBAwdUo0YN7+PIyEht27bNp8btdsvtdvss+/HHHyVJLpcr8E0Gmfzjx/yqy83N1cFfTvpdG2r8HZt0anwnjx33uzZQAvXeBcPYjAjU2I7kB8fn3erj81eofS6NCMT7dro+UKz8nWJEqP3OBfLvulAanxlji46Olt1uqX92A5Ikm8fj8ZjdRHl5+eWXdeLECY0YMUKSNG/ePH3xxReaOHGit2batGnKyMgwq0UAAABcYNasWaPatWub3QZQ7iz1XyRRUVHatGmT9/GBAwcUGRnpU5OSkqKEhASfZQUFBdq7d6/q1q2r8PDwgPfpcrmUnJysOXPmKDo6OuDPV5GsPDbJ2uNjbKHLyuNjbKHLyuOz8tgka4/PrLFZ7XUETrNUmLz11ls1bdo0HTlyRFWqVNHKlSv15JNP+tQ4nU45nc5S215zzTUV1aZXdHS0Zf+Xyspjk6w9PsYWuqw8PsYWuqw8PiuPTbL2+Kw8NqAiWSpMRkVFacSIEerTp48KCwuVlJSkxo0bm90WAAAAAFiOpcKkJMXFxSkuLs7sNgAAAADA0ix1axAAAAAAQMUgTJrA6XRq8ODBZZ67GeqsPDbJ2uNjbKHLyuNjbKHLyuOz8tgka4/PymMDzGCpW4MAAAAAACoGM5MAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIkwAAAAAAwwiTAAAAAADDCJMAAAAAAMMIk5KKioqUm5uroqIis1sBAAAAgJBAmJTkcrnUvn17uVwus1sBAAAAgJBAmAQAAAAAGEaYBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBAAAAAAYRpgEAAAAABhGmAQAAAAAGEaYBACgHJUUFQakFgCAYGM3uwEAAKwkzO7Q5rT+ftU2HzkjwN0AABA4zEwCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMI0wCAAAAAAwjTAIAAAAADCNMAgAAAAAMs1yYXLt2rRITE9WxY0c99dRTZrcDAAAAAJZkqTC5d+9ejR8/XtOnT9eSJUv01Vdfad26dWa3BQAAAACWYze7gfK0atUqde7cWdHR0ZKk9PR0VapUyeSuAAAAAMB6LBUm9+zZI4fDoQceeEAHDx5U27ZtNXz4cJ8at9stt9vts8zlclVglwAAAAAQ+iwVJouLi7Vp0ybNnj1bVatW1aBBg5SVlaXExERvTWZmpjIyMkzsEgAAAABCn6XC5BVXXKGYmBhddtllkqT27dtr27ZtPmEyJSVFCQkJPtu5XC4lJydXaK8AAAAAEMosFSbbtm2rUaNGye1266KLLtJHH32k9u3b+9Q4nU45nU6TOgQAAAAAa7BUmGzSpIn69++v3r17q7CwULfddpu6d+9udlsAAAAAYDmWCpOSlJSUpKSkJLPbAAAAAABLs9R9JgEAAAAAFYMwCQAAAAAwjDAJAAAAADCMMAkAAAAAMIwwCQAAAAAwjDAJAAAAADAsKMPkCy+8UGrZU089VfGNAAAAAADKFFT3mXzxxRfldru1fPly5eXleZcXFhZq7dq1Sk1NNbE7AAAAAMBpQRUmmzRpoi+++EJhYWGqXr26d3l4eLimTZtmXmMAAAAAAB9BFSbbtGmjNm3aqHXr1mrcuLF3eWFhoRwOh4mdAQAAAAB+KyjPmSwoKND06dNVUFCghIQE3XzzzVq+fLnZbQEAAAAA/isow+Szzz6rm266SatXr9YVV1yhZcuW6bXXXjO7LQAAAADAfwVlmCwuLtatt96q7Oxs3Xnnnapdu7ZKSkrMbgsAAAAA8F9BGSZLSkq0bds2ffjhh7rtttv0zTffqLCw0Oy2AAAAAAD/FVQX4DntoYce0iOPPKKkpCTVrl1b7dq10+OPP252WwAAAACA/wrKMBkbG6vY2FgVFRWpsLBQq1atUnh4uNltAQAAAAD+KygPcz18+LAGDBigm266SY0bN1a/fv20f/9+s9sCAAAAAPxXUIbJiRMnqkmTJsrOzlZ2drZuvvlmTZgwwey2AAAAAAD/FZRhMicnR4MHD5bT6dSll16qoUOH6ocffjC7LQAAAADAfwVlmCwqKlJ+fr738YkTJ2Sz2UzsCAAA4H8Kivy/yryRWgAIJUF5AZ7OnTurb9++SkxMlCQtWLBAd911l8ldAQAAnBJhd6jvrGF+1b7eb2qAuwEAcwRlmHz44YcVHR2tjz76SCUlJUpMTFRSUpLZbQEAAAAA/ivowuQ333yjnJwc3X777erevbvZ7QAAAAAAyhBU50y+9957+stf/qJXX31V3bp10/r1681uCQAAAABQhqCamZw9e7aWLFmiqKgoff7550pPT9ftt99udlsAAAAAgN8JqplJSYqKipIkNW3aVEePHjW5GwAAAABAWYIqTP7+9h/h4eEmdQIAAAAAOJugCpO/x70lAQAAACA4BdU5kzt37lSzZs28j0+ePKlmzZrJ4/HIZrNpy5YtJnYHAAAAADgtqMLkqlWrzG4BAAAAAOCHoAqTtWrVMrsFAAAAAIAfgvqcSQAAAABAcCJMAgAAAAAMI0wCAAAAAAwLqnMmT9u4caOmTZumn3/+WR6Px7t8yZIlJnYFAAAAADgtKMPkxIkT1b17d/3pT3/iXpMAAAAAEISCMkw6HA7169fP7DYAAAAAAGcQlOdM1q9fXzt37jS7DQAAAADAGQTlzOTevXvVvXt3XXnllapUqZJ3OedMAgAAAEBwCMowOWLEiD+8j2eeeUZHjx7VlClTyqEjAAAAAMBvBeVhri1atFClSpW0ceNGffzxx95l/tqwYYOysrIC1R4AAAAAXPCCMkwuXLhQQ4cO1c8//6xff/1VjzzyiObOnevXtseOHVN6eroeeuihMte73W7l5ub6/HG5XOXZPgAAAABYXlAe5vr6669r3rx5ioyMlCQNGDBADzzwgO65555zbjtu3DiNGDFC+/btK3N9ZmamMjIyyrVfAACAYFZSVKgwu8Pv+oKiAkXYI/ysLVSEgX0DsI6gDJMlJSXeIClJUVFRCgs79yTqvHnzVLNmTcXExGjBggVl1qSkpCghIcFnmcvlUnJy8h9rGgAAIEiF2R3anNbf7/rmI2eo76xhftW+3m/q+bYFIMQFZZisXr26Vq9erTvvvFOStHr1al1yySXn3G758uU6ePCg4uPj9fPPP+v48eN6+umnNXbsWG+N0+mU0+kMWO8AAAAAcCEIyjD5xBNPaNCgQXryySclSQ6HQy+99NI5t5s1a5b35wULFmjjxo0+QRIAAAAAUD6CMkw6nU6tWLFCOTk5KikpUb169ZSTk2N2WwAAAACA/wqqq7keO3ZMx44d04ABA5SXl6fLL79cNWrU0LFjxzRo0CBD+0pMTOQekwAAAAAQIEE1M/nII4947yvZsmVL7/Lw8HB16NDBrLYAAAAAAL8TVGFy5syZkqQxY8Zo8uTJJncDAAAAADiToAqTpyUmJuqzzz7zPrbZbKpcubLq1q2ratWqmdgZAAAAAEAK0jA5efJkff3117ruuusUHh6ub775RjVq1NCJEyc0adIk7y1DAAAAAADmCKoL8Jx25ZVXatasWVq8eLGysrL09ttvq1mzZlq0aJFftwgBAAAAAARWUIbJvXv3+lyAp3HjxsrJyVF0dLSJXQEAAAAATgvKMGm327V+/Xrv4/Xr18vhcOjIkSMqKioysTMAAAAAgBSk50yOHz9eQ4cOlc1mU0lJiSpVqqQXX3xRM2bMUM+ePc1uDwAAAAAueEEZJhs3bqw1a9bom2++UXh4uK699lqFh4erYcOGZrcGAAAAAFCQhsmDBw/q3Xff1bFjx3yWp6ammtMQAAAAAMBHUIbJESNG6OKLL9af/vQn2Ww2s9sBAAAAAPxOUIbJw4cP68033zS7DQAAAADAGQTl1VyvvPJKHT9+3Ow2AAAAAABnEJQzk5GRkbr77rvVokULVa5c2buccyYBAAAAIDgEZZisVauWatWqZXYbAAAAAIAzCMowOXjwYLNbAAAAAACcRVCFyV69euntt99W06ZNy7yK65YtW0zoCgAAAADwe0EVJqdOnSpJWrp0qcmdAAAAAADOJqjCZGRkpKRT50xu3rxZR44ckcfj8a7nPEoAv1VSVKgwu6PcawGUj4KiQkX4+XtnpDZQ+J64MITa5xIIZkEVJk97/PHH9e9//1t169b1LrPZbIqNjTWvKQBBJ8zu0Oa0/n7VNh85I8DdAPi9CLtDfWcN86v29X5TA9zNufGdcmEItc8lEMyCMkxu2LBBq1at8rktCAAAAAAgeISZ3UBZLr/8coIkAAAAAASxoJqZXLlypSSpXr16Gjx4sDp37iy7/X8tcpgrAAAAAASHoAqTs2fP9nn89ttve3/mnEkAAAAACB5BHSYlyePxqKioSA4HV9IC8P/t3XtcVHX+x/H3xCUtJHQLzTQzTcNctcda4A2VVAoDQ9QQtdLMzGuuZqwpXdTNNMIWVrfWakUxKgUvK3nXEDVNcxfNLGyD1FQ0UlKS28zvD2J+UYYcA87M+Ho+Hj2Sc74zvD+cOQc+53znDAAAAByFQ75ncu/evVqwYIGKiorUv39/dezYUWlpaWbHAgAAAAD8xCGbyXnz5qlDhw7atGmTbrzxRq1du1Zvv/222bEAAAAAAD9xyGaytLRUnTt31s6dO9WrVy81adJEVqvV7FgAAAAAgJ84ZDNptVqVmZmpbdu2qUuXLvryyy9VXFxsdiwAAAAAwE8c6gY85UaPHq3JkydrwIABatKkiYKCgvTcc8+ZHQsAAAAA8BOHbCb79OlT4WNANm7cKDc3NxMTAQAAAAB+ziGbyezsbC1dulQFBQWy2WyyWq3KyclRcnKy2dEAAAAAAHLQ90xOnjxZxcXF2r9/v2655RYdOXJErVq1MjsWAAAAAOAnDtlMXrhwQS+++KK6du2qwMBAvfPOO/rss8/MjgUAAAAA+IlDNpM+Pj6SpGbNmikrK0ve3t58NAgAAAAAOBCHfM9ks2bNNHv2bIWHh+u5555TQUGBioqKzI4FAAAAAPiJQ12ZPH/+vCTphRdeUMeOHdWmTRsNHDhQH3/8sV566SWT0wEAAAAAyjnUlcmAgAD96U9/Uo8ePdS9e3dJUlRUlKKiokxOBgAAAAD4OYdqJtPT0/Xxxx9r165dSkpKksViUffu3dWjRw/de++98vT0vOxzJCQk6MMPP5Qkde/eXVOnTq3p2AAAAABw1XGoZrJBgwYKCQlRSEiIJOn48ePauXOnXn31VeXk5Gj//v2VPn7nzp3KyMhQamqqLBaLRo4cqY0bN6p37961ER8AAAAArhoO1UyWO3bsmDZv3qwdO3bo0KFDuuuuuzRo0KDLPu6mm25SdHS0/QpmixYt9O2339Z0XAAAAAC46jhUMxkXF6ctW7bowoUL6tatm6KiohQQEKA6depU6fF33HGH/d/Z2dlKS0tTcnJyhTH5+fnKz8+vsOzkyZO/PzwAAAAAXEUcqpl84403FBQUpFGjRqlDhw5X/DxZWVl68skn9eyzz+q2226rsG7x4sVKSEj4fUEBkxUVl8rTw61KY0uLi+Tmcfn3G0uStaRY17h7/J5ocAKlRcVy86zadjYy1hG4cm2OguMEHBGvS8AcDtVMrlu3Tlu3blVsbKyys7PVpUsX9ejRQ127dpWXl1eVnmPfvn2aMGGCpk2bpr59+/5q/aOPPqrw8PAKy06ePKkhQ4ZUSw1AbfD0cFPU1KQqjV02d4j2zR1ZpbF/mrro98SCk3Dz9FDaI8OrNDYk8Z0aTlO9XLk2R3GNuwfHFDgcXpeAORyqmbzttts0fPhwDR8+XPn5+UpPT9fGjRv18ssv6/bbb9c771T+i//EiRMaO3as4uLi1KlTp0uO8fb2lre3d03EBwAAAICrhkM1kz/37bffKi8vT0VFRfLw8JCb2+Wn9L311lsqLCzUnDlz7MsiIyM1ePDgmowKAAAAAFcdh2omExMTtWfPHn3yySfy8fFRt27dNGDAAAUEBOjaa6+97OOnT5+u6dOn10JSAAAAALi6OVQzuX37dgUGBuqZZ55Rs2bNzI4DAAAAAPgNDtVM/vOf/zQ7AgAAAACgCq4xOwAAAAAAwPnQTAIAAAAADKOZBAAAAAAYRjMJAAAAADCMZhIAAAAAYBjNJAAAAADAMJpJAAAAAIBhNJMAAAAAAMNoJgEAAAAAhtFMAgAAAAAMo5kEAAAAABhGMwkAAAAAMIxmEgAAAABgGM0kAAAAAMAwmkkAAAAAgGE0kwAAAAAAw2gmAQAAAACG0UxeRlFxaY2MheOwlhTX6HhXVVpU9Z+DkbGujmNKGSP7kSvvc0UGajMy1nAOB3it1dQxxZVrcxSuXJ8r1wZUB3ezAzg6Tw83RU1NqtLYZXOH1HAa1IRr3D20b+7IKo//09RFNZjGebh5eijtkeFVGhuS+E4Np3EeHFPKGNnvXHmf83T30GPvTKzS2H8Nf73mcjjA67KmjimuXJujcOX6XLk2oDpwZRIAAAAAYBjNJAAAAADAMJpJAAAAAIBhNJMAAAAAAMNoJgEAAAAAhtFMAgAAAAAMo5kEAAAAABhGMwkAAAAAMIxmEgAAAABgGM0kAAAAAMAwmkkAAAAAgGE0kwAAAAAAw2gmAQAAAACG0UwCAAAAAAyjmQQAAAAAGEYzCQAAAAAwjGYSAAAAAGAYzSQAAAAAwDCXaybXrFmjkJAQ9e7dW0lJSWbHAQAAAACX5G52gOp06tQpxcXFKSUlRZ6enoqMjJS/v79atmxpdjQAAAAAcCku1Uzu3LlTAQEB8vHxkSQFBwdr3bp1GjdunH1Mfn6+8vPzKzzu+PHjkqSTJ09e8nkLC85W6fsfO3bMeGg4hNM/XKzyWEfZzkZel1Wtz2hteYU187xG1FRtNckRjimuvO2crbaLZwuqPLYm1dQxxUh9NbXtXLk2I4z+rnPV+syorVGjRnJ3d6k/uwFJksVms9nMDlFd3njjDRUUFGjSpEmSpA8++ECZmZmaOXOmfUx8fLwSEhLMiggAAICrzObNm9WkSROzYwDVzqVOkVyqL7ZYLBW+fvTRRxUeHl5hWVFRkY4eParbbrtNbm5uNZpRKrsCOmTIECUlJalRo0Y1/v1qkyvXJrl2fdTmvFy5PmpzXq5cnyvXJrl2fWbV5mo/R6CcSzWTDRs21N69e+1f5+bmytfXt8IYb29veXt7/+qxt99+e43n+6VGjRq57FkqV65Ncu36qM15uXJ91Oa8XLk+V65Ncu36XLk2oDa51N1cO3furF27dikvL08//vijNmzYoMDAQLNjAQAAAIDLcbkrk5MmTdIjjzyi4uJiDRgwQO3atTM7FgAAAAC4HJdqJiUpNDRUoaGhZscAAAAAAJfmUtNcnYW3t7fGjRt3yfduOjtXrk1y7fqozXm5cn3U5rxcuT5Xrk1y7fpcuTbADC710SAAAAAAgNrBlUkAAAAAgGE0kwAAAAAAw2gma1hKSoqio6PNjlGtjh07prZt26pfv34V/jtx4sQlx8fHxys+Pr6WU16ZY8eOqXXr1oqJiamw/PPPP1fr1q2VkpJiUrLq9eWXX6p169Zav3692VGqxdWy3STXPKb80uVqjI6Odrpt6mr7nCStW7dO/fv3V1hYmEJDQ7Vo0SKzI1W7Cxcu6MUXX1Tv3r0VFhamqKgo7dq16zfH//DDDxozZkwtJrwy5cfMHTt2VFgeFBSkY8eOmZSqevzyb5Tg4GBNmDBBZ86cMTsa4JJc7m6uqB2+vr5atWqV2TFqhI+Pj7Zv367S0lK5ublJktLS0tSgQQOTk1WflJQUBQcHKzk5WcHBwWbHqRZXw3aD83K1fe7UqVN65ZVXlJKSovr16+vChQsaNmyYmjdvrvvuu8/seNXCZrNp9OjR8vPz09q1a+Xp6alDhw5p1KhRio2Nlb+//68ec+7cOR0+fNiEtMZ5eHhoxowZWr16tby8vMyOU61+/jeKzWbTa6+9pgkTJmjZsmUmJwNcD1cma8mePXs0ePBghYeHKygoSB9++KGksjPss2bN0uDBgxUUFKQVK1aYnPTKnTlzRmPGjFH//v0VERGhnTt32tdlZmZq4MCB6tu3rxYvXmxiysu7/vrr5efnp08++cS+bMeOHercubMkaenSpRo4cKAefPBBhYaG6quvvpJUdkb36aefVnBwsL777jtTsldFSUmJVq9erUmTJunQoUP65ptvJJXlnzVrlh566CE99NBDOnTokCRp2LBhGjdunIKDg/X555+bGb1SV7Lddu3apcjISPv41NRUPf/887We/UoMGzZMu3fvllR2Jj4oKEiSax1TfqtGZ1PZPld+FWj37t0aNmyYpLKrmP3791e/fv00c+ZM9e7d27Tsv+X7779XcXGxLl68KKls/5szZ45atmypzMxM+++7ESNG6OjRo5LKtufzzz+v8PBwhYSEKCMjw8wSLmvPnj369ttv9Ze//EWenp6SpDZt2uipp57SggUL9Pnnn2vgwIEKDQ3V0KFDdfLkSc2aNUu5ubkaO3asyekvz9fXV507d9Yrr7zyq3X/+Mc/FBISotDQUM2ZM0elpaV6+eWX9dZbb9nHTJgwQRs2bKjNyFfEYrFo/PjxysrK0uHDh/Xmm28qPDxcYWFhmjt3rsrvQ/mvf/1LwcHBCgkJ0bx580xODTgPmslasnTpUs2aNUupqamaPXu2FixYYF938uRJLVu2TAsXLtTcuXNNTFl1ubm5Faa4Llq0SLNnz1ZERIRSUlK0cOFCxcTE6Pz585Kk06dPa/HixXrvvfeUlJTk0E2JJD3wwAP26WiZmZlq3bq1PDw8dP78eW3atElLlizRv//9b/Xq1avCmc7AwECtX79ef/jDH8yKflnbtm1T48aN1bx5c/Xq1UvJycn2dT4+Plq5cqUmTJigZ5991r68fHqen5+fGZGrzOh2CwgI0OnTp+1/3Kempqp///5mllAtnPGY4soq2+cuJTo6WhMnTtSqVavUtGlTlZaW1lLSqrvzzjt13333qVevXhowYIDmzZsnq9Wqm2++WdOnT1dsbKxSU1M1fPhwzZgxw/64oqIipaamKjY2VtHR0SoqKjKxisodOHBAbdu2lcViqbD8nnvu0YEDBzRlyhSNGTNGa9asUUhIiBYvXqzp06fL19dXf//7301KbUx0dLQyMjIqTHf96KOPtGXLFqWkpCg1NVU5OTlKTk5Wv379tHbtWknS+fPn9emnn6pHjx4mJTfG09NTzZo10+HDh3Xw4EEtX75cK1eu1KlTp7R69WplZmZq2bJlWr58uVavXq3PPvtMBw8eNDs24BSY5lpL5s2bp61bt2rdunX673//qwsXLtjXdenSRRaLRa1atdLZs2fNC2nApaa5+vv763//+5/+9re/SSo7G19+RjokJETXXXedJKlnz57as2ePQzcmPXv21Pz582W1WvXhhx/qgQceUFpamry8vBQbG6u1a9cqOztb27dvr1BH+/btTUxdNSkpKXrwwQcllW2XKVOm6Omnn5YkDRo0SFLZFZPo6Gjl5eVJktq1a2dKVqOMbjeLxaLw8HCtXr1a/fv313fffecU2/BynPGY4soq2+d+6ezZszp+/Li6d+8uSYqIiFBiYmJtRTXkxRdf1JgxY5SRkaGMjAwNGjRIo0aN0tGjR/XUU0/Zx5WfVJT+/xjj5+enm266SV988YX++Mc/1nr2qrBYLJds5IuLi1VaWqrTp0+rZ8+ekqSoqChJcrr3G3p5eWnmzJn26a5S2VXyvn37qk6dOpLKXoMrV67UkCFDVFRUpJycHO3fv189e/a0X7F1BhaLRYmJicrLy7OfNLx48aIaN26sM2fOqGfPnqpXr56ksquUAKqGZrIG7N27V02bNlXDhg1ls9nk5uamqKgo+fv7y9/fX506ddKUKVPs46+99lpJ+tXZT2djtVq1ePFi+fj4SCp7T82NN96oTZs2yd39/19qNputwteOyMvLS3feeaf27dunjz/+WJMnT1ZaWppOnDihhx9+WEOHDlVgYKBuvPHGCldZy7elo/ruu++Unp6ugwcPKjExUTabTfn5+fapSj/fLlar1f7ew/I/KhzdlWy38PBwjRw5Up6enurXr5/JFVzapY4pkuzTs0pKSiqMd8ZjitEancXl9rlf1ufm5iZn+Pjnbdu2qaCgQCEhIYqIiFBERITef/99rVmzRk2aNLGfbCwtLa1w45Py7SqVHWMc+XdB+/bttWTJEhUXF8vDw8O+/D//+Y/atWunL774wr6ssLBQubm5TrXPlevatWuF6a5Wq/VXY8pfn2FhYUpLS9P+/fv1xBNP1GrO36OoqEhff/21/P39FRoaquHDh0uS8vPz5ebmpuXLl1cYf+rUKdWtW1fe3t5mxAWcCtNca8CKFSu0adMmSdIXX3yhpk2bKjs7WxMnTlT37t21Y8cOh5y29HsFBATYp3weOXJEYWFh+vHHHyVJ69evV1FRkc6dO6etW7cqICDAzKhV8sADDyg2NlZt27a1/8Fz3XXXqVmzZnrsscfUvn17paenO9W2XL16tQICApSenq4tW7Zo69atGj16tN577z1Jsk9h2rhxo1q0aKEbbrjBzLhXxOh2u+WWW9SoUSP7NC5HdKljSv369XXkyBFJsq9zZq5aY2X73M/r27x5sySpXr16uvXWW/XRRx9JktasWWNa9srUqVNHsbGx9itxNptNR44cUYcOHXTu3Dnt3btXUtl2/fnJ07S0NEllU0jz8/PVqlWr2g9fRR07dlTLli3117/+VcXFxZKkgwcPauHChRo3bpwaNWpknx66atUqvf7663J3d3fKEx/l011zc3MVEBCgtWvX6uLFiyopKdGKFSvsv7NDQ0OVlpamnJwcdezY0eTUVWO1WhUfH6/27dsrIiJCq1at0oULF1RSUqKxY8dq/fr16tixo9LT0+3LJ0+ezDRXoIoc95SgExs1apSmTp2qpUuXqlGjRpo/f77Onj2rvn37ysvLSx06dNDFixdVUFBgdtRqNX36dMXExCg0NFSSNHfuXPsd4ho3bqzIyEgVFhbqySefVIsWLcyMWiU9e/bUc889p4kTJ9qXeXh4yGq1KiQkRJ6enmrXrp2ysrJMTGlMSkqKJk2aVGFZVFSUFi1aJC8vL3366adavny56tatqzlz5piU8ve5ku0WEhKiDRs2qGHDhmZEvqxLHVNycnIUHR2tFStWuMTdM121xsr2ualTp2r27NlKSEhQ165d7etfeeUVTZs2TfPnz1fr1q0dcmZAQECAxo0bp9GjR9sbrW7dumn8+PEKCgrS7NmzVVhYKC8vrwo3eDl69KjCw8MlSXFxcRWuVDqihIQExcXF6cEHH5Sbm5tuuOEGzZs3T/7+/po3b55eeOEFzZ07V/Xr17f/v3Hjxho2bJiWLFlidvwqK5/u+vjjj6tHjx7Kz89XRESESkpK1K1bNw0dOlSSdPPNN6t+/frq0KGDQ1+FLb+vg1TWTPr5+Sk2NlY+Pj46fPiwBg0apNLSUnXr1k3h4eGyWCwaOnSoIiMjZbVa1bt3b/vN2wBUzmJzhvk0AGpcUFCQEhMT1aRJE7Oj1KqSkhJNnTpV999/v/r06WN2HEAJCQkaNGiQfH19tWHDBq1Zs8ZpPqu3MuV3hr7UR2oAAJwTVyYBXLVsNpu6deumzp07q1evXmbHASSVzeQYMWKE3N3d5e3trdmzZ5sdCQCAS+LKJAAAAADAMG7AAwAAAAAwjGYSAAAAAGAYzSQAAAAAwDCaSQAAAACAYTSTAHCV++CDD5SUlCRJevfdd/Xmm2/W+PfMy8tT69atLztu27Ztev3112s8DwAAMI6PBgGAq9y+fft0xx13SJIGDx5scpqKDhw4oHPnzpkdAwAAXALNJAA4gd27d+u1116Tr6+vsrKyVLduXY0fP15LlizR119/rT59+mjatGnasmWLFi5cqOLiYtWpU0fPPvus7r77bsXHx+v48eM6ffq0jh8/rgYNGiguLk6ZmZnasmWLduzYoTp16igvL0/ff/+9YmJilJWVpZdeeklnz56VxWLRiBEj9NBDD2n37t2Ki4tT06ZNlZWVpaKiIsXExCggIKDSGjZs2KC4uDjVrVtXbdu2tS8vKCjQCy+8oOzsbJ07d07XX3+9Xn31Vf3www9KTk5WaWmp6tWrp0mTJumDDz7Qu+++K6vVKh8fH82YMUMtWrSo6R8/AAC4BJpJAHASBw4c0PLly9WmTRuNHDlSb775phITE3X+/HkFBgaqV69eiouLU2JiourXr6+srCwNHz5cGzZskCTt3btXK1eulJeXl0aPHq333ntPEyZM0ObNm3XHHXdoyJAhio+PlySVlJToqaee0tSpU9WnTx+dOnVKAwcOVLNmzSRJmZmZev755+Xn56e3335bCQkJlTaTZ86c0bRp05ScnKyWLVvqjTfesK9LT0+Xt7e33n//fUlSTEyMkpKSNGPGDEVGRur777/XpEmTtGfPHq1cuVJJSUmqW7euMjIyNH78eKWlpdXUjxwAAFSCZhIAnESTJk3Upk0bSdKtt96qevXqydPTUw0aNND111+vw4cPKzc3V4899pj9MRaLRd98840k6d5775WXl5ckqU2bNpVOH83OzlZhYaH69OkjSWrYsKH69Omj7du3y9/fX40bN5afn5/9uVJTUyvNvm/fPrVq1UotW7aUJD388MN67bXXJEn333+/mjZtqiVLlignJ0d79uzR3Xff/avn2LZtm3JychQZGWlfdu7cOZ09e1Y+Pj6Vfn8AAFD9aCYBwEl4enpW+NrdveIh3GKxqFOnTpo/f7592YkTJ+Tr66uNGzeqTp06FcbabLbf/F5Wq/VXy2w2m0pKSiTJ0HNdaszPsy9btkzvv/++hgwZotDQUPn4+OjYsWOXzNSvXz8988wz9q9zc3N1ww03VPq9AQBAzeBurgDgIu655x7t2LFDX331lSTpo48+UlhYmAoLCyt9nJubm71JLNe8eXN5eHjYp8ieOnVK69evV+fOna8oW8eOHXXkyBEdPnxYkpSSkmJfl5GRofDwcA0cOFDNmzfXli1bVFpa+qtsXbp00dq1a5Wbmyup7M6zjz766BXlAQAAvx9XJgHARVxzzTV66aWX9Oc//1k2m03u7u5auHChrrvuukofFxgYqJkzZ1ZY5uHhoQULFmjWrFmKj49XaWmpxo4dq4CAAO3evdtwtgYNGujVV1/VlClT5OHhoXvuuce+bsSIEYqJiVFKSorc3Nx011136csvv5QkderUSePHj5eHh4dmzJihJ554QiNGjJDFYpGXl5cSEhJksVgM5wEAAL+fxXa5uUkAAAAAAPwCVyYBANVi0aJFWrNmzSXXPf744woLC6vlRAAAoCZxZRIAAAAAYBg34AEAAAAAGEYzCQAAAAAwjGYSAAAAAGAYzSQAAAAAwDCaSQAAAACAYf8HpoGBkXjYE0cAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = news.groupby([pd.Grouper(key='mention_date', freq='M'), 'journal', 'outlet'])['doi'].nunique().unstack().fillna(0)\n", + "sns.set(font_scale=1)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "\n", + "fig, axes = plt.subplots(df.shape[1], 1, figsize = (13,14))\n", + "for i, c in enumerate(df.columns):\n", + " ax = axes[i]\n", + " df2 = df[c].reset_index() \n", + " sns.barplot(x='mention_date', y=c, hue='journal', data=df2, ax=ax)\n", + "\n", + " ax.spines['right'].set_visible(False)\n", + " ax.spines['top'].set_visible(False) \n", + " \n", + " if i != 2: \n", + " ax.get_legend().remove()\n", + " else:\n", + " ax.legend(loc='lower left', bbox_to_anchor=[1,0])\n", + " if i < (len(df.columns) - 1): \n", + " ax.set(xticklabels=[]) \n", + " ax.set_xlabel(None) \n", + " ax.set_ylabel('\\n'.join(ax.get_ylabel().split(' ')))\n", + " else: \n", + " ax.set_xticklabels([datetime.strptime(m.get_text()[0:7], '%Y-%m').strftime('%b') for m in ax.get_xticklabels()]\n", + ")\n", + "title = 'Number of unique research articles (DOIs) covered by outlet that month'\n", + "\n", + "# ax.set(xticklabels=[\"%s, %s\" % x for x in tmp.index]) \n", + "# fig.suptitle(title, fontsize=14)\n", + "display(md(\"## \" + title))\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/research_by_outlet_and_journal.jpg')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "## Number of stories by outlet" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = news.groupby([pd.Grouper(key='mention_date', freq='M'), 'journal', 'outlet']).size().unstack().fillna(0)\n", + "\n", + "fig, axes = plt.subplots(df.shape[1], 1, figsize = (13,14))\n", + "for i, c in enumerate(df.columns):\n", + " ax = axes[i]\n", + " df2 = df[c].reset_index() \n", + " sns.barplot(x='mention_date', y=c, hue='journal', data=df2, ax=ax)\n", + "\n", + " ax.spines['right'].set_visible(False)\n", + " ax.spines['top'].set_visible(False) \n", + " \n", + " if i != 2: \n", + " ax.get_legend().remove()\n", + " else:\n", + " ax.legend(loc='lower left', bbox_to_anchor=[1,0])\n", + " if i < (len(df.columns) - 1): \n", + " ax.set(xticklabels=[]) \n", + " ax.set_xlabel(None) \n", + " ax.set_ylabel('\\n'.join(ax.get_ylabel().split(' ')))\n", + " else: \n", + " ax.set_xticklabels([datetime.strptime(m.get_text()[0:7], '%Y-%m').strftime('%b') for m in ax.get_xticklabels()]\n", + ")\n", + " \n", + "# ax.set(xticklabels=[\"%s, %s\" % x for x in tmp.index]) \n", + "title = 'Number of stories by outlet'\n", + "display(md(\"## \" + title))\n", + "# fig.suptitle('Number of stories by outlet', fontsize=14)\n", + "plt.tight_layout()\n", + "\n", + "plt.savefig('figures/stories_by_outlet_and_journal.jpg')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysis of Number of Users" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "def summarize_users(setA, setB, labels):\n", + " fieldsA = list(set(['doi', 'news_url']).intersection(setA.columns))\n", + " fieldsB = list(set(['doi', 'news_url']).intersection(setB.columns)) \n", + "\n", + " user_field = set(setA.columns).intersection(['user_id_str', 'accountId']).pop()\n", + " \n", + "\n", + " df = setA[fieldsA + [user_field]].nunique().to_frame(labels[0]) \\\n", + " .merge(setB[fieldsB + [user_field]].nunique().to_frame(labels[1]) \\\n", + " , how='outer', left_index=True, right_index=True).T\n", + "\n", + " for c in set(fieldsA).union(fieldsB):\n", + " df['users_per_%s' % c] = df[user_field].divide(df[c])\n", + " \n", + " df[fieldsA] = df[fieldsA].fillna(0).astype(int)\n", + " df[fieldsB] = df[fieldsB].fillna(0).astype(int)\n", + " df[user_field] = df[user_field].astype(int)\n", + " \n", + " setA = set(setA[user_field].astype(str))\n", + " setB = set(setB[user_field].astype(str))\n", + " intersection = len(setA.intersection(setB))\n", + " df['Percent Intersection'] = df[user_field].map(lambda x: intersection*100.0/x)\n", + " \n", + " return df\n", + "\n", + "\n", + "def draw_user_venn(setA, setB, labels, filename = None):\n", + " user_field = set(setA.columns).intersection(['user_id_str', 'accountId']).pop()\n", + " \n", + " setA = set(setA[user_field].astype(str))\n", + " setB = set(setB[user_field].astype(str))\n", + "\n", + " plt.figure(figsize=(8,6))\n", + " fig = venn2(subsets = tuple(map(len, (setA.difference(setB), setB.difference(setA), setA.intersection(setB)))), set_labels = labels, subset_label_formatter=lambda x: f'{x:,}')\n", + " plt.tight_layout()\n", + " if filename: \n", + " plt.savefig(filename)\n", + " return fig\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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doinews_urluser_id_strusers_per_news_urlusers_per_doiPercent Intersection
r325027771nan85.4513.99
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" + ], + "text/plain": [ + " doi news_url user_id_str users_per_news_url \\\n", + "r 325 0 27771 nan \n", + "n 200 486 60296 124.07 \n", + "\n", + " users_per_doi Percent Intersection \n", + "r 85.45 13.99 \n", + "n 301.48 6.44 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "summarize_users(research_tweets, news_tweets, ['r', 'n'])" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "As shown in Figure 3, the overlap between the social media audiences of first- and second-order citations was very small on both Twitter (14.0% of 27,771 who shared research and 6.4% of 60,296 accounts that shared news stories) and Facebook (22.6% of 3,976 that shared research and 10.9% of 8,193 spaces that shared news stories).\n" + ] + } + ], + "source": [ + "sentence = '''\n", + "As shown in Figure 3, the overlap between the social media audiences of first- and \n", + "second-order citations was very small on both Twitter ({:.1f}% of {:,} who shared research \n", + "and {:.1f}% of {:,} accounts that shared news stories) and Facebook ({:.1f}% of {:,} that shared \n", + "research and {:.1f}% of {:,} spaces that shared news stories).\n", + "'''.format(\n", + " len(set(research_tweets.user_id_str).intersection(news_tweets.user_id_str))*100/len(research_tweets.user_id_str.unique()),\n", + " len(research_tweets.user_id_str.unique()),\n", + " len(set(news_tweets.user_id_str).intersection(research_tweets.user_id_str))*100/len(news_tweets.user_id_str.unique()),\n", + " len(news_tweets.user_id_str.unique()),\n", + " len(set(research_fb.accountId).intersection(news_fb.accountId))*100/len(research_fb.accountId.unique()),\n", + " len(research_fb.accountId.unique()),\n", + " len(set(news_fb.accountId).intersection(research_fb.accountId))*100/len(news_fb.accountId.unique()),\n", + " len(news_fb.accountId.unique())\n", + " \n", + "\n", + "\n", + ")\n", + "\n", + "print(sentence.replace('\\n', ''))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = (18, 18)\n", + "sns.set(font_scale=1.4)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "setA = news_tweets\n", + "setB = research_tweets\n", + "\n", + "# display(md('### Twitter users for research and News'))\n", + "# display(summarize_users(setA, setB, ('News', 'Research')))\n", + "\n", + "fig = draw_user_venn(setB, setA, ('First Order\\nCitations', 'Second Order\\nCitations'), 'figures/venn_twitter.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mongeon, P., Bowman, T., & Costas, R. (2022). Open dataset of scholars on Twitter [Data set]. *Zenodo*. [https://doi.org/10.5281/zenodo.7013518](https://doi.org/10.5281/zenodo.7013518)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "researchers_tw = pd.read_csv('data/authors_tweeters_2022_08_21.csv')\n", + "researchers_tw['tweeter_id'] = researchers_tw['tweeter_id'].astype(str)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s1 = set(researchers_tw.tweeter_id)\n", + "s2 = set(news_tweets.user_id_str)\n", + "s3 = set(research_tweets.user_id_str)\n", + "\n", + "# (Set1,Set2,1n2,Set3,1n3,2n3,1n2n3)\n", + "venn3(subsets=tuple(map(len, (s1-s2-s3, \n", + " s2-s1-s3, \n", + " s1.intersection(s2)-s3,\n", + " s3-s1-s2,\n", + " s1.intersection(s3)-s2,\n", + " s2.intersection(s3)-s1,\n", + " s1.intersection(s2).intersection(s3)))),\n", + " set_labels=('Researcher Tweeters', 'Second Order', 'First Order'),\n", + " subset_label_formatter=lambda x: f'{x:,}')\n", + "\n", + "plt.savefig('figures/venn_researcher_tweeters.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Of the 423,920 Twitter accounts identified as belonging to a researcher by Mongeon, Bowman & Costas (2022), we found small overlaps with the accounts that made first- and second-order citations. Of the 27,771 accounts that made first-order citations, 3,899 (14.0%) were known researchers, while this number was only 3,830 (6.4%) of the 60,296 accounts that made second-order citations. Only 718 accounts were in all three groups. \n" + ] + } + ], + "source": [ + "sentence = '''Of the {:,} Twitter accounts identified as belonging to a researcher by \n", + "Mongeon, Bowman & Costas (2022), we found small overlaps with the accounts that \n", + "made first- and second-order citations. \n", + "Of the {:,} accounts that made first-order citations, {:,} ({:.1f}%) were known researchers, \n", + "while this number was only {:,} ({:.1f}%) of the {:,} accounts that made second-order citations. \n", + "Only {:,} accounts were in all three groups. \n", + "'''.format(\n", + " len(s1), \n", + " len(s3), len(s1.intersection(s3)), 100*(len(s1.intersection(s3)))/len(s3), \n", + " len(s1.intersection(s2)), 100*(len(s1.intersection(s2)))/len(s2), len(s2), \n", + " len(s1.intersection(s2).intersection(s3))\n", + ")\n", + "\n", + "print(sentence.replace('\\n', ''))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### FB users for research and News" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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accountIddoinews_urlusers_per_news_urlusers_per_doiPercent Intersection
News819321651615.8837.9310.95
Research39762460nan16.1622.56
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" + ], + "text/plain": [ + " accountId doi news_url users_per_news_url \\\n", + "News 8193 216 516 15.88 \n", + "Research 3976 246 0 nan \n", + "\n", + " users_per_doi Percent Intersection \n", + "News 37.93 10.95 \n", + "Research 16.16 22.56 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('### FB users for research and News'))\n", + "plt.rcParams['figure.figsize'] = (18, 18)\n", + "sns.set(font_scale=1.4)\n", + "sns.set_style(\"ticks\")\n", + "\n", + "setA = news_fb\n", + "setB = research_fb\n", + "\n", + "display(summarize_users(setA, setB, ('News', 'Research')))\n", + "\n", + "draw_user_venn(setB, setA, ('First Order\\nCitations', 'Second Order\\nCitations'), 'figures/venn_facebook.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_order_twsecond_order_twfirst_order_fbsecond_order_fb
count27,771.0060,296.003,976.008,193.00
mean1.851.981.642.19
std4.823.702.663.75
min1.001.001.001.00
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\n", + "
" + ], + "text/plain": [ + " first_order_tw second_order_tw \\\n", + "count 27,771.00 60,296.00 \n", + "mean 1.85 1.98 \n", + "std 4.82 3.70 \n", + "min 1.00 1.00 \n", + "25% 1.00 1.00 \n", + "50% 1.00 1.00 \n", + "75% 2.00 2.00 \n", + "max 379.00 254.00 \n", + "\n", + " first_order_fb second_order_fb \n", + "count 3,976.00 8,193.00 \n", + "mean 1.64 2.19 \n", + "std 2.66 3.75 \n", + "min 1.00 1.00 \n", + "25% 1.00 1.00 \n", + "50% 1.00 1.00 \n", + "75% 2.00 2.00 \n", + "max 120.00 133.00 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = research_tweets.groupby('user_id_str').size().describe().to_frame()\n", + "df.columns = ['first_order_tw']\n", + "\n", + "df['second_order_tw'] = news_tweets.groupby('user_id_str').size().describe()\n", + "df['first_order_fb'] = research_fb.groupby('accountId').size().describe()\n", + "df['second_order_fb'] = news_fb.groupby('accountId').size().describe()\n", + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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linkis_researchertweets_rankntweetslikes_ranknlikesretweets_ranknretweetsorder
username
bmj_latesthttps://twitter.com/bmj_latestFalse1379541068127263first
AndersJonitahttps://twitter.com/AndersJonitaFalse2239162787236421first
uhiimanhttps://twitter.com/uhiimanFalse3236370690216360second
Thomas_Wilckenshttps://twitter.com/Thomas_WilckensTrue4226512406403211both
BangoBillyhttps://twitter.com/BangoBillyFalse5197339664286312both
outbreakscihttps://twitter.com/outbreaksciFalse6194710111395010first
BendallJanehttps://twitter.com/BendallJaneFalse71791378111117158both
Artaudculationhttps://twitter.com/ArtaudculationFalse81731369112131749both
pash22https://twitter.com/pash22True9134476441298299both
tonto_1964https://twitter.com/tonto_1964False101321403108117158first
EricTopolhttps://twitter.com/EricTopolTrue37641022953810914both
apoorva_nychttps://twitter.com/apoorva_nycTrue4163351607420876both
trishgreenhalghhttps://twitter.com/trishgreenhalghTrue6653151780899588both
DrEricDinghttps://twitter.com/DrEricDingTrue27226446702515736both
Karl_Lauterbachhttps://twitter.com/Karl_LauterbachFalse49117923297234435both
GeorgeMonbiothttps://twitter.com/GeorgeMonbiotFalse59815730507615706second
carolecadwallahttps://twitter.com/carolecadwallaFalse22336632878715229both
HillaryClintonhttps://twitter.com/HillaryClintonFalse934422102570323407second
neal_katyalhttps://twitter.com/neal_katyalFalse934421122183108808second
BarackObamahttps://twitter.com/BarackObamaFalse1976811106785225146second
JoeBidenhttps://twitter.com/JoeBidenFalse197681825617138211second
\n", + "
" + ], + "text/plain": [ + " link is_researcher \\\n", + "username \n", + "bmj_latest https://twitter.com/bmj_latest False \n", + "AndersJonita https://twitter.com/AndersJonita False \n", + "uhiiman https://twitter.com/uhiiman False \n", + "Thomas_Wilckens https://twitter.com/Thomas_Wilckens True \n", + "BangoBilly https://twitter.com/BangoBilly False \n", + "outbreaksci https://twitter.com/outbreaksci False \n", + "BendallJane https://twitter.com/BendallJane False \n", + "Artaudculation https://twitter.com/Artaudculation False \n", + "pash22 https://twitter.com/pash22 True \n", + "tonto_1964 https://twitter.com/tonto_1964 False \n", + "EricTopol https://twitter.com/EricTopol True \n", + "apoorva_nyc https://twitter.com/apoorva_nyc True \n", + "trishgreenhalgh https://twitter.com/trishgreenhalgh True \n", + "DrEricDing https://twitter.com/DrEricDing True \n", + "Karl_Lauterbach https://twitter.com/Karl_Lauterbach False \n", + "GeorgeMonbiot https://twitter.com/GeorgeMonbiot False \n", + "carolecadwalla https://twitter.com/carolecadwalla False \n", + "HillaryClinton https://twitter.com/HillaryClinton False \n", + "neal_katyal https://twitter.com/neal_katyal False \n", + "BarackObama https://twitter.com/BarackObama False \n", + "JoeBiden https://twitter.com/JoeBiden False \n", + "\n", + " tweets_rank ntweets likes_rank nlikes retweets_rank \\\n", + "username \n", + "bmj_latest 1 379 5 41068 1 \n", + "AndersJonita 2 239 1627 87 2364 \n", + "uhiiman 3 236 37069 0 21636 \n", + "Thomas_Wilckens 4 226 512 406 403 \n", + "BangoBilly 5 197 339 664 286 \n", + "outbreaksci 6 194 7101 11 3950 \n", + "BendallJane 7 179 1378 111 1171 \n", + "Artaudculation 8 173 1369 112 1317 \n", + "pash22 9 134 476 441 298 \n", + "tonto_1964 10 132 1403 108 1171 \n", + "EricTopol 37 64 10 22953 8 \n", + "apoorva_nyc 41 63 3 51607 4 \n", + "trishgreenhalgh 66 53 15 17808 9 \n", + "DrEricDing 272 26 4 46702 5 \n", + "Karl_Lauterbach 491 17 9 23297 23 \n", + "GeorgeMonbiot 598 15 7 30507 6 \n", + "carolecadwalla 2233 6 6 32878 7 \n", + "HillaryClinton 9344 2 2 102570 3 \n", + "neal_katyal 9344 2 11 22183 10 \n", + "BarackObama 19768 1 1 106785 2 \n", + "JoeBiden 19768 1 8 25617 13 \n", + "\n", + " nretweets order \n", + "username \n", + "bmj_latest 27263 first \n", + "AndersJonita 21 first \n", + "uhiiman 0 second \n", + "Thomas_Wilckens 211 both \n", + "BangoBilly 312 both \n", + "outbreaksci 10 first \n", + "BendallJane 58 both \n", + "Artaudculation 49 both \n", + "pash22 299 both \n", + "tonto_1964 58 first \n", + "EricTopol 10914 both \n", + "apoorva_nyc 20876 both \n", + "trishgreenhalgh 9588 both \n", + "DrEricDing 15736 both \n", + "Karl_Lauterbach 4435 both \n", + "GeorgeMonbiot 15706 second \n", + "carolecadwalla 15229 both \n", + "HillaryClinton 23407 second \n", + "neal_katyal 8808 second \n", + "BarackObama 25146 second \n", + "JoeBiden 8211 second " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def which_order(user_id_str):\n", + " f = user_id_str in set(research_tweets.user_id_str)\n", + " s = user_id_str in set(news_tweets.user_id_str)\n", + " if f and s: \n", + " return 'both'\n", + " elif f:\n", + " return 'first'\n", + " elif s:\n", + " return 'second'\n", + "\n", + "df2 = research_tweets.append(news_tweets)\n", + "df2['is_researcher'] = df2.user_id_str.isin(researchers_tw.tweeter_id.astype(str))\n", + "\n", + "df = df2.groupby('user_id_str')[['tweet_id', 'nlikes', 'nretweets', 'is_researcher']].agg({'tweet_id': 'nunique', 'nlikes': 'sum', 'nretweets': 'sum', 'is_researcher': 'sum'}).astype(int)\n", + "df.columns = ['ntweets', 'nlikes', 'nretweets', 'is_researcher']\n", + "df['tweets_rank'] = df.ntweets.rank(ascending=False, method='min').astype(int)\n", + "df['likes_rank'] = df.nlikes.rank(ascending=False, method='min').astype(int)\n", + "df['retweets_rank'] = df.nretweets.rank(ascending=False, method='min').astype(int)\n", + "df = df[(df.tweets_rank <= 10) | (df.likes_rank <= 10) | (df.retweets_rank <= 10)].join(df2[['user_id_str', 'username']].drop_duplicates().set_index('user_id_str'))\n", + "df['is_researcher'] = df['is_researcher'].astype(bool)\n", + "df['link'] = df.username.map(lambda x: 'https://twitter.com/%s' % x)\n", + "df['order'] = df.index.map(which_order)\n", + "df.set_index('username', inplace=True)\n", + "df.sort_values('tweets_rank', inplace=True)\n", + "\n", + "df = df[['link', 'is_researcher', 'tweets_rank', 'ntweets', 'likes_rank', 'nlikes', 'retweets_rank', 'nretweets', 'order']]\n", + "display(df)\n", + "df.to_clipboard()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntweetsnlikesnretweetstweets_ranklikes_rankretweets_rank
username
Thomas_Wilckens2264062111152122
pash22134441299214298
aruberutou9450323793446
mancunianmedic86108240145773
EricTopol642295310914533
apoorva_nyc635160720876611
drjohnmorley6346376683421
JuanGrvas572751878199132
InstituteHPE55234091059403
carlzimmer541019539461078
drpatfarrell541981012031066
trishgreenhalgh531780895881264
DrEricDing2646702157362822
theAliceRoberts206696298344109
michaelmina_lab1172912902110910
doctormacias921656877314345
raoult_didier318620805764756
globalhlthtwit37836474664787
\n", + "
" + ], + "text/plain": [ + " ntweets nlikes nretweets tweets_rank likes_rank \\\n", + "username \n", + "Thomas_Wilckens 226 406 211 1 152 \n", + "pash22 134 441 299 2 142 \n", + "aruberutou 94 5 0 3 2379 \n", + "mancunianmedic 86 1082 401 4 57 \n", + "EricTopol 64 22953 10914 5 3 \n", + "apoorva_nyc 63 51607 20876 6 1 \n", + "drjohnmorley 63 46 37 6 683 \n", + "JuanGrvas 57 275 187 8 199 \n", + "InstituteHPE 55 23 40 9 1059 \n", + "carlzimmer 54 10195 3946 10 7 \n", + "drpatfarrell 54 19 8 10 1203 \n", + "trishgreenhalgh 53 17808 9588 12 6 \n", + "DrEricDing 26 46702 15736 28 2 \n", + "theAliceRoberts 20 6696 2983 44 10 \n", + "michaelmina_lab 11 7291 2902 110 9 \n", + "doctormacias 9 21656 8773 143 4 \n", + "raoult_didier 3 18620 8057 647 5 \n", + "globalhlthtwit 3 7836 4746 647 8 \n", + "\n", + " retweets_rank \n", + "username \n", + "Thomas_Wilckens 122 \n", + "pash22 98 \n", + "aruberutou 3446 \n", + "mancunianmedic 73 \n", + "EricTopol 3 \n", + "apoorva_nyc 1 \n", + "drjohnmorley 421 \n", + "JuanGrvas 132 \n", + "InstituteHPE 403 \n", + "carlzimmer 8 \n", + "drpatfarrell 1066 \n", + "trishgreenhalgh 4 \n", + "DrEricDing 2 \n", + "theAliceRoberts 9 \n", + "michaelmina_lab 10 \n", + "doctormacias 5 \n", + "raoult_didier 6 \n", + "globalhlthtwit 7 " + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = research_tweets.append(news_tweets)\n", + "df2 = df2[df2.user_id_str.isin(researchers_tw.tweeter_id.astype(str))]\n", + "\n", + "df = df2.groupby('user_id_str')[['tweet_id', 'nlikes', 'nretweets']].agg({'tweet_id': 'nunique', 'nlikes': 'sum', 'nretweets': 'sum'}).astype(int)\n", + "df.columns = ['ntweets', 'nlikes', 'nretweets']\n", + "df['tweets_rank'] = df.ntweets.rank(ascending=False, method='min').astype(int)\n", + "df['likes_rank'] = df.nlikes.rank(ascending=False, method='min').astype(int)\n", + "df['retweets_rank'] = df.nretweets.rank(ascending=False, method='min').astype(int)\n", + "df = df[(df.tweets_rank <= 10) | (df.likes_rank <= 10) | (df.retweets_rank <= 10)].join(df2[['user_id_str', 'username']].drop_duplicates().set_index('user_id_str')).set_index('username')\n", + "df.sort_values('tweets_rank')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntweetsnlikesnretweetstweets_ranklikes_rankretweets_rank
username
uhiiman2360012519014749
_be_el_tee_1152810225222633
FloLake944027319581332
TimeToSayGood1594139743800650
samturpin943031075414749
MedPierre932421627801579
sapiopath88217125169804
denio_vale82117848893588
pash22711941499622367
Thomas_Wilckens701205910877768
apoorva_nyc5951487208382733
washingtonpost351477370661041310
DrEricDing22454361499820645
GeorgeMonbiot15305071570634454
Karl_Lauterbach13211514108421818
nytimes71556683061029117
JoeNBC316795669632311012
maddow2181417467547999
neal_katyal2221838808547976
HillaryClinton210257023407547922
BarackObama1106785251461240311
JoeBiden12561782111240368
\n", + "
" + ], + "text/plain": [ + " ntweets nlikes nretweets tweets_rank likes_rank \\\n", + "username \n", + "uhiiman 236 0 0 1 25190 \n", + "_be_el_tee_ 115 28 10 2 2522 \n", + "FloLake 94 40 27 3 1958 \n", + "TimeToSayGood15 94 139 74 3 800 \n", + "samturpin 94 3 0 3 10754 \n", + "MedPierre 93 24 21 6 2780 \n", + "sapiopath 88 2 1 7 12516 \n", + "denio_vale 82 117 84 8 893 \n", + "pash22 71 194 149 9 622 \n", + "Thomas_Wilckens 70 120 59 10 877 \n", + "apoorva_nyc 59 51487 20838 27 3 \n", + "washingtonpost 35 14773 7066 104 13 \n", + "DrEricDing 22 45436 14998 206 4 \n", + "GeorgeMonbiot 15 30507 15706 344 5 \n", + "Karl_Lauterbach 13 21151 4108 421 8 \n", + "nytimes 7 15566 8306 1029 11 \n", + "JoeNBC 3 16795 6696 3231 10 \n", + "maddow 2 18141 7467 5479 9 \n", + "neal_katyal 2 22183 8808 5479 7 \n", + "HillaryClinton 2 102570 23407 5479 2 \n", + "BarackObama 1 106785 25146 12403 1 \n", + "JoeBiden 1 25617 8211 12403 6 \n", + "\n", + " retweets_rank \n", + "username \n", + "uhiiman 14749 \n", + "_be_el_tee_ 2633 \n", + "FloLake 1332 \n", + "TimeToSayGood15 650 \n", + "samturpin 14749 \n", + "MedPierre 1579 \n", + "sapiopath 9804 \n", + "denio_vale 588 \n", + "pash22 367 \n", + "Thomas_Wilckens 768 \n", + "apoorva_nyc 3 \n", + "washingtonpost 10 \n", + "DrEricDing 5 \n", + "GeorgeMonbiot 4 \n", + "Karl_Lauterbach 18 \n", + "nytimes 7 \n", + "JoeNBC 12 \n", + "maddow 9 \n", + "neal_katyal 6 \n", + "HillaryClinton 2 \n", + "BarackObama 1 \n", + "JoeBiden 8 " + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = news_tweets.copy()\n", + "df = df2.groupby('user_id_str')[['tweet_id', 'nlikes', 'nretweets']].agg({'tweet_id': 'nunique', 'nlikes': 'sum', 'nretweets': 'sum'}).astype(int)\n", + "df.columns = ['ntweets', 'nlikes', 'nretweets']\n", + "df['tweets_rank'] = df.ntweets.rank(ascending=False, method='min').astype(int)\n", + "df['likes_rank'] = df.nlikes.rank(ascending=False, method='min').astype(int)\n", + "df['retweets_rank'] = df.nretweets.rank(ascending=False, method='min').astype(int)\n", + "df = df[(df.tweets_rank <= 10) | (df.likes_rank <= 10) | (df.retweets_rank <= 10)].join(df2[['user_id_str', 'username']].drop_duplicates().set_index('user_id_str')).set_index('username')\n", + "df.sort_values('tweets_rank')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntweetsnlikesnretweetstweets_ranklikes_rankretweets_rank
username
uhiiman2360012519014749
_be_el_tee_1152810225222633
FloLake944027319581332
TimeToSayGood1594139743800650
samturpin943031075414749
MedPierre932421627801579
sapiopath88217125169804
denio_vale82117848893588
pash22711941499622367
Thomas_Wilckens701205910877768
apoorva_nyc5951487208382733
washingtonpost351477370661041310
DrEricDing22454361499820645
GeorgeMonbiot15305071570634454
Karl_Lauterbach13211514108421818
nytimes71556683061029117
JoeNBC316795669632311012
maddow2181417467547999
neal_katyal2221838808547976
HillaryClinton210257023407547922
BarackObama1106785251461240311
JoeBiden12561782111240368
\n", + "
" + ], + "text/plain": [ + " ntweets nlikes nretweets tweets_rank likes_rank \\\n", + "username \n", + "uhiiman 236 0 0 1 25190 \n", + "_be_el_tee_ 115 28 10 2 2522 \n", + "FloLake 94 40 27 3 1958 \n", + "TimeToSayGood15 94 139 74 3 800 \n", + "samturpin 94 3 0 3 10754 \n", + "MedPierre 93 24 21 6 2780 \n", + "sapiopath 88 2 1 7 12516 \n", + "denio_vale 82 117 84 8 893 \n", + "pash22 71 194 149 9 622 \n", + "Thomas_Wilckens 70 120 59 10 877 \n", + "apoorva_nyc 59 51487 20838 27 3 \n", + "washingtonpost 35 14773 7066 104 13 \n", + "DrEricDing 22 45436 14998 206 4 \n", + "GeorgeMonbiot 15 30507 15706 344 5 \n", + "Karl_Lauterbach 13 21151 4108 421 8 \n", + "nytimes 7 15566 8306 1029 11 \n", + "JoeNBC 3 16795 6696 3231 10 \n", + "maddow 2 18141 7467 5479 9 \n", + "neal_katyal 2 22183 8808 5479 7 \n", + "HillaryClinton 2 102570 23407 5479 2 \n", + "BarackObama 1 106785 25146 12403 1 \n", + "JoeBiden 1 25617 8211 12403 6 \n", + "\n", + " retweets_rank \n", + "username \n", + "uhiiman 14749 \n", + "_be_el_tee_ 2633 \n", + "FloLake 1332 \n", + "TimeToSayGood15 650 \n", + "samturpin 14749 \n", + "MedPierre 1579 \n", + "sapiopath 9804 \n", + "denio_vale 588 \n", + "pash22 367 \n", + "Thomas_Wilckens 768 \n", + "apoorva_nyc 3 \n", + "washingtonpost 10 \n", + "DrEricDing 5 \n", + "GeorgeMonbiot 4 \n", + "Karl_Lauterbach 18 \n", + "nytimes 7 \n", + "JoeNBC 12 \n", + "maddow 9 \n", + "neal_katyal 6 \n", + "HillaryClinton 2 \n", + "BarackObama 1 \n", + "JoeBiden 8 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = news_tweets.copy()\n", + "df = df2.groupby('user_id_str')[['tweet_id', 'nlikes', 'nretweets']].agg({'tweet_id': 'nunique', 'nlikes': 'sum', 'nretweets': 'sum'}).astype(int)\n", + "df.columns = ['ntweets', 'nlikes', 'nretweets']\n", + "df['tweets_rank'] = df.ntweets.rank(ascending=False, method='min').astype(int)\n", + "df['likes_rank'] = df.nlikes.rank(ascending=False, method='min').astype(int)\n", + "df['retweets_rank'] = df.nretweets.rank(ascending=False, method='min').astype(int)\n", + "df = df[(df.tweets_rank <= 10) | (df.likes_rank <= 10) | (df.retweets_rank <= 10)].join(df2[['user_id_str', 'username']].drop_duplicates().set_index('user_id_str')).set_index('username')\n", + "df.sort_values('tweets_rank')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### Twitter users for preprint and journal research" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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doiuser_id_strusers_per_doiPercent Intersection
Preprint Research152480231.5919.99
Journal Research17323929138.324.01
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" + ], + "text/plain": [ + " doi user_id_str users_per_doi \\\n", + "Preprint Research 152 4802 31.59 \n", + "Journal Research 173 23929 138.32 \n", + "\n", + " Percent Intersection \n", + "Preprint Research 19.99 \n", + "Journal Research 4.01 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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doiaccountIdusers_per_doiPercent Intersection
Preprint Research967507.8126.53
Journal Research150342522.835.81
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news_urldoiuser_id_strusers_per_news_urlusers_per_doiPercent Intersection
News about Preprint Research31110546479149.45442.669.99
News about Journal Research199951846292.77194.3425.16
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('### Twitter users for News that covered preprint and journal research'))\n", + "\n", + "setA = news_tweets[news_tweets.is_preprint]\n", + "setB = news_tweets[~news_tweets.is_preprint]\n", + "\n", + "display(summarize_users(setA, setB, ('News about Preprint Research', 'News about Journal Research')))\n", + "\n", + "ax = draw_user_venn(setA, setB, ('News about\\nPreprint Research', 'News about\\nJournal Research'))\n", + "\n", + "None" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "### FB Spaces with News that covered preprint and journal research" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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News about Preprint Research311107623520.0558.2712.98
News about Journal Research220109276712.5825.3929.24
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(md('### FB Spaces with News that covered preprint and journal research'))\n", + "\n", + "setA = news_fb[news_fb.is_preprint]\n", + "setB = news_fb[~news_fb.is_preprint]\n", + "\n", + "display(summarize_users(setA, setB, ('News about Preprint Research', 'News about Journal Research')))\n", + "\n", + "ax = draw_user_venn(setA, setB, ('News about\\nPreprint Research', 'News about\\nJournal Research'))\n", + "\n", + "None" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/README.md b/README.md index 69cd1a3..4669827 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,17 @@ -# second-order-effects -An examination of the social media attention received by news stories that cover research (2nd order effects in Altmetrics) +# Data collection and analysis code for: Second-order Citations in Altmetrics +*Juan Pablo Alperin* + +Last updated: March 31, 2023 + +This repository houses the data and code used to analyze the second order effects of a sample of COVID-19 related articles. The accompanying publication can be found here: + + ## Related Publication +Alperin, J.P., Fleerackers, A., Riedlinger, M. & Haustein, S. (2023). Second-order Citations in Altmetrics: A Case Study Analyzing the Audiences of COVID-19 Research in the News and on Social Media. *bioarxiv*. + + +## Data files +See the following dataset for data and details. The files in this repository should be placed inside a `data/` folder. + +Alperin, J.P. (2023). Data on first- and second-Order citations for sample of COVID-19 research. *Harvard Dataverse*. [https://doi.org/10.7910/DVN/OEKB01](https://doi.org/10.7910/DVN/OEKB01) + + diff --git a/figures/Social media posts of news stories by outlet.png b/figures/Social media posts of news stories by outlet.png new file mode 100644 index 0000000..d03ffe1 Binary files /dev/null and b/figures/Social media posts of news stories by outlet.png differ diff --git a/figures/Social media posts of research articles by journal.png b/figures/Social media posts of research articles by journal.png new file mode 100644 index 0000000..ac82380 Binary files /dev/null and b/figures/Social media posts of research articles by journal.png differ diff --git a/figures/pairplot.png b/figures/pairplot.png new file mode 100644 index 0000000..4c4f1be Binary files /dev/null and b/figures/pairplot.png differ diff --git a/figures/research_by_outlet_and_journal.jpg b/figures/research_by_outlet_and_journal.jpg new file mode 100644 index 0000000..6577948 Binary files /dev/null and b/figures/research_by_outlet_and_journal.jpg differ diff --git a/figures/stories_by_outlet_and_journal.jpg b/figures/stories_by_outlet_and_journal.jpg new file mode 100644 index 0000000..09c5a65 Binary files /dev/null and b/figures/stories_by_outlet_and_journal.jpg differ diff --git a/figures/venn_facebook.png b/figures/venn_facebook.png new file mode 100644 index 0000000..d1dcbdb Binary files /dev/null and b/figures/venn_facebook.png differ diff --git a/figures/venn_researcher_tweeters.png b/figures/venn_researcher_tweeters.png new file mode 100644 index 0000000..0c45b6d Binary files /dev/null and b/figures/venn_researcher_tweeters.png differ diff --git a/figures/venn_twitter.png b/figures/venn_twitter.png new file mode 100644 index 0000000..2271db9 Binary files /dev/null and b/figures/venn_twitter.png differ