Predicting the Political Alignment of Twitter Users

By Michael D. Conover, Bruno Gonçalves, Jacob Ratkiewicz, Alessandro Flammini, Filippo Menczer. 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing, 2011

657 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a115c5b0ce8ca5679324ed895e044d3215f74087

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Citations in the corpus

Predicting political preference of Twitter users (2013, methodology): “Happy birthday to Her Majesty, Queen Elizabeth II, Queen of Canada.”
Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors (2012, methodology): “In (Conover et al. 2011a), this was done by identifying strong partisan clusters in the Twitter network prior to building user feature vectors.”
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media (2021, background): “Second, because distinguishing between political subreddits can be seen as a proxy for text-based ideology prediction, which is a well-established NLP task (e.g. Conover et al., 2011; Iyyer et al., 2014; Kannangara, 2018; Xiao et al., 2020).”
Sensing Trending Topics in Twitter (2013, background): “of opinion formation around major political themes [4] offer”
Gender Inference of Twitter Users in Non-English Contexts (2013, background): “It is remarkable, then, that advances in latent attribute inference on social media have been largely conﬁned to English content, e.g. (Liu and Ruths, 2013; Zamal et al., 2012; Pennacchiotti and Popescu, 2011; Conover et al., 2011a).”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, methodology): “[5] inferred the political alignment of Twitter users, based on the tweets or hashtags posted by the users, as well as the community structure of diffusion networks (via retweets) of political information, observing that the mechanism relying on social network performs better.”
Classifying Political Orientation on Twitter: It's Not Easy! (2013, methodology): “While this method did not achieve the best performance to date (93% vs. 95%), it was benchmarked on a less restrictive dataset than (Conover et al. 2011b) which likely contributed to the small different in performance.”
Filter Bubbles, Echo Chambers, and Fake News: How Social Media Conditions Individuals to Be Less Critical of Political Misinformation (2021, background): “Additionally, these networks often replicate preexisting social networks (Boyd & Ellison, 2008) and tend to be politically homogeneous (Conover et al., 2011), suggesting that questionable material is rarely challenged when one’s network is made up of like-minded peers.”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, background): “These applications include healthcare [1], network intrusion detection systems[2], spam and fraud detection, phishing detection [3], political decision making [4], adversarial advertisement detection [5], and financial engineering [6].”
Partisan asymmetries in online political activity (2012, background): “…support the claim that sampling based on this set of hashtags produces a representative set of political tweets, we selected all the tweets in the gardenhose from the study period that included any one of 2500 hand-selected political keywords related to the 2010 elections (Ratkiewicz et al., 2011).”
Quantifying Political Leaning from Tweets and Retweets (2013, methodology): “Regarding Twitter, political polarization was studied in (Conover et al. 2011b).”
Characterizing the 2016 Russian IRA influence campaign (2018, methodology): “These effects are documented by several studies Bessi and Ferrara (2016); Conover et al. (2011a); El-Khalili (2013); Ferrara (2017); Fourney et al. (2017); Ratkiewicz et al. (2011a); Shorey and Howard (2016); Woolley and Howard (2016).”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, background): “In [45] a rigorous dataset was constructed to determine and politically rank individuals for the US 2010 midterm elections, based on the political discussion and network-based data available on their Twitter timelines.”