Making Sense of Social Data

• NYU - ITP
• Fall 2015
• Gilad Lotan | danah boyd

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Description

Data are created and collected all around us, trails left from interactions in social media, accessible through streams, feeds, APIs, and data-stores. These data are used to power a growing number of services, modeled not only off our own interactions but also interactions of our friends and larger network of connections. While well intended, and many times well functioning, the growing range of uses of systems that algorithmically score content means there are a growing number of unintended consequences and inherent biases. In order to untangle some of these issues, we’ll dive into the literature, while building our own algorithmically-driven data services.

In this class we will explore various computational and social science approaches to understanding networked users. We’ll collect data by talking to people, as well as use Python scripts to access data from APIs such as Twitter and Instagram. We’ll learn how to make sense of these different data, touching topics such as qualitative interviewing, ethnographic observation, content analysis, natural language processing, content classification, authority ranking, and clustering. We’ll also be using a number of open source tools that help us make sense of networks, including Gephi and Python’s networkx library. And we'll be diving into literature from various fields - including sociology and media studies - to make sense of social data that we gather along the way.

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Schedule

• [[9/4] (https://github.com/giladlotan/itpmssd/blob/master/Week_1/README.md)] 1. Class Intro: Models, Python, Amazon AWS
• [[9/11] (https://github.com/giladlotan/itpmssd/blob/master/Week_2/README.md)] 2. Participant-Observation, Loading data into pandas DataFrames
• [[9/18] (https://github.com/giladlotan/itpmssd/blob/master/Week_3/README.md)] 3. Networked Publics, Instagram/Twitter APIs, Simple Stats
• [[9/25] (https://github.com/giladlotan/itpmssd/blob/master/Week_4/README.md)] 4. Social Network Analysis, Networkx, Gephi
• [[10/2] (https://github.com/giladlotan/itpmssd/blob/master/Week_5/README.md)] 5. Interviews, Text Analysis
• [[10/9] (https://github.com/giladlotan/itpmssd/blob/master/Week_6/README.md)] 6. Trust & Regulation | Recommender Systems
• [10/16] ---> NO CLASS
• [[10/23] (https://github.com/giladlotan/itpmssd/blob/master/Week_7/README.md)] 7. Power & Manipulation | Intro to Bots
• [[10/30] (https://github.com/giladlotan/itpmssd/blob/master/Week_8/README.md)] 8. Data Visualization
• [11/6] ---> NO CLASS
• [[11/13] (https://github.com/giladlotan/itpmssd/blob/master/Week_9/README.md)] 9. Bot Assignment Due
• [[11/20] (https://github.com/giladlotan/itpmssd/blob/master/Week_10/README.md)] 10. Classification, or Modern Day Magic
• [11/27] ---> NO CLASS
• [[12/4] (https://github.com/giladlotan/itpmssd/blob/master/Week_11/README.md)] 11. Topic TBD | Unintended Consequences
• [[12/11] (https://github.com/giladlotan/itpmssd/blob/master/Week_12/README.md)] 12. Present Final Projects

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Logistics

• Class: Fridays, 9am-12pm
• Office Hours: by request 30 minutes before or afer class or via Skype
• Slack: itpmssd.slack.com
  • If you haven't already signed up to our Slack group, please do so here - [itpmssd.slack.com] (https://itpmssd.slack.com).
  • We'll be using Slack for class-related communications: questions, thoughts, answers. We'll also use it to share interesting and relevant articles we find. Most importantly, we'll be relying on Slack to create and test our Bot creations. (More on that l8trz)
  • Private communications (e.g., class absences, personal concerns) should be communicated through email.
• Assignments:
  • Reading:
    • Each week, there will be some form of reading. We are keeping the reading short and accessible so please do it.
    • The required readings are available via Dropbox or through an online link, as indicated in the assignment section of each week.
    • We strongly recommend acquiring a copy of ["Learning from Strangers"] (http://www.amazon.com/Learning-Strangers-Qualitative-Interview-Studies/dp/0684823128/)
    • We recommend acquiring a copy of ["Data Science from Scratch"] (http://www.amazon.com/Data-Science-Scratch-Principles-Python/dp/149190142X) as a supplemental resource for all technical topics covered in class.
  • Coding
    • We'll use a shared dropbox folder for submitting coding homework assignments.
• Evaluation:
  • On-time attendance and class participation: 25%
  • Assignments: 25%
  • Mid-term Bot Project: 25%
  • Final Project: 25%
  • Note: Showing up more than 10 minutes late without prior notice is an unexcused absence. More than 2 unexcused absences results in automatic failure.