To analyze the Dominion Voting Scandal in 2016 using Twitter API data and network analyses.
Given that the data involves users, their tweets and interactions on the Twitter social media platform, we will explore this social network through four main methods:
- Hegselmann-Krause model of Opinion Dynamics
- Centrality Measures
- Community Exploration
- Sentiment Analysis
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https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00286-y} "Characterizing networks of propaganda on twitter: a case study"
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https://journals.sagepub.com/doi/full/10.1177/2056305117691545} "Classifying Twitter Topic-Networks Using Social Network Analysis"
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https://ieeexplore.ieee.org/abstract/document/6830323?casa_token=j8TSDoyoCFwAAAAA:535bDbPMzyCx2KFugukHDK7nkDgbWoBEPHipVlCHlFBlko30J2KK2BBUWwqQYjn83h36fCVvcUc} "Sentiment analysis on Twitter"
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https://dl.acm.org/doi/abs/10.1145/2666310.2666366?casa_token=SSgPntbRuSkAAAAA:AmL2tJeQeS2HXzxts8cxKs1fXKtW_xMhwRE0KG2r0d3p1clF5ajq-ZBRxPSskgqbywGgqqZ2vBtpSpA} "A search and summary application for traffic events detection based on Twitter data"
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https://link.springer.com/chapter/10.1007/978-981-15-2740-1_17} "Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model"
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https://www.sciencedirect.com/science/article/pii/S1877050916309644} "Psychological Warfare Analysis Using Network Science Approach"
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https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00446-2}"Sentiment and structure in word co-occurrence networks on Twitter"
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https://arxiv.org/abs/2005.00635#}"Using Noisy Self-Reports to Predict Twitter User Demographics"