Partisan asymmetries in online political activity

By Michael D. Conover, Bruno Gonçalves, Alessandro Flammini, Filippo Menczer. EPJ Data Science, 2012

322 citations recorded by the source corpus.

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered, partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/09c206e26c379b5ba852557363cd6415acb38368

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Citations in the corpus

#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, background): “[19] found that right-leaning Twitter users, compared to left-leaning ones, exhibit greater levels of political activity, tighter social bonds, and a communication network topology that facilitates rapid dissemination of information.”
Political Fact-Checking on Twitter: When Do Corrections Have an Effect? (2018, background): “Similarly, one could try and infer a Twitter user’s political preference and look at whether two users are in the same political camp, regardless of their dyadic relationship (Conover et al., 2012) This approach brings its own limitation, as it is not clear that individuals who do not know one anoth”
Filter Bubbles, Echo Chambers, and Fake News: How Social Media Conditions Individuals to Be Less Critical of Political Misinformation (2021, background): “Platforms like Facebook take advantage of social networks that are often dense and highly clustered (Conover et al., 2012).”
Tweeting From Left to Right (2015, result): “One possible explanation for the variability in results is that some studies have relied on self-selected samples of partisan individuals, whereas others have not (Bakshy et al., 2015; Conover et al., 2012; Westfall et al., 2015).”
The spread of low-credibility content by social bots (2017, unknown): “These include information overload and finite attention [41], novelty of false news [57], the selective exposure [52, 40, 39] caused by polarized and segregated online social networks [9, 8], algorithmic popularity bias [45, 20, 37], and other cognitive vulnerabilities such as confirmation bias and ”
Limited individual attention and online virality of low-quality information (2017, background): “segregated communities that we typically observe around discussions of polarizing topics [13, 14].”
Measuring Online Social Bubbles (2015, background): “On Twitter, political polarization is even more evident (Conover et al., 2011; Conover et al., 2012).”