Political Polarization on Twitter

By Michael D. Conover, Jacob Ratkiewicz, Matthew R. Francisco, Bruno Gonçalves, Filippo Menczer, Alessandro Flammini. International Conference on Web and Social Media, 2011

1,746 citations recorded by the source corpus.

In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9a7ae269358c6366cc46dc924d63b47ba405d082

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Citations in the corpus

Predicting political preference of Twitter users (2013, background): “Related work also includes interaction-driven approaches to study sentiments expressed implicitly in the form of preferential following [6], [13], and retweeting [4].”
On the Behaviour of Deviant Communities in Online Social Networks (2016, positive): “…(Barbieri, Bonchi, and Manco 2013), focusing on the structural and dynamical properties of speciﬁc topical communities such as groups supporting political parties (Conover et al. 2011), or discussion groups about ru-mors, hoaxes (Ratkiewicz et al. 2011) and conspiracy theories (Bessi et al. 2015).”
Quantifying Controversy in Social Media (2015, methodology): “most recent papers have focused on Twitter [8, 15, 24, 25], controversy in other social-media platforms, such as blogs [1] and opinion fora [2], have also been analyzed.”
Political Effects of the Internet and Social Media (2020, positive): “Similarly, Conover et al. (2011) study links between political tweets in the six weeks leading up to the 2010 U.”
Echoes of power: language effects and power differences in social interaction (2011, background): “h on-line and off-line conversational settings. 7. FURTHER RELATED WORK In the opening sections, we have discussed some of the ways in which earlier work used text content to analyze on-line networks [49, 31, 11, 9, 16], as well as background on language coordination and the exchange-theoretic notio”
Computational Fact Checking from Knowledge Networks (2015, background): “This is somehow surprising, given that in transforming a directed graph into undirected we are destroying potentially useful information.”
Adaptive Method for Following Dynamic Topics on Twitter (2014, background): “…task are news monitoring (Magdy, 2013), crisis management (Vieweg et al., 2010), customer feedback detection (Chen at al., 2013), public political polarization analysis (Conover, 2011), election results prediction (Tumasjan et al., 2010), and public healthcare monitoring tools (Ali et al., 2013).”
Limited individual attention and online virality of low-quality information (2017, background): “This may further lead to polarization [57, 13, 56, 43]; one group may automatically discount ideas from another [39, 44].”
Geometric deep learning (2016, background): “in the context of political discourse (Conover et al. (2011)) and might be related to ‘echo chamber’ theories that attempt to explain the reasons for the difference in fake and true news propagation patterns.”
Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior (2012, background): “Given the nature of political polarization which has been shown to also prevail in Twitter communication [9], sentiment associated with certain political topics, political parties or politicians might play an even more important role, particularly in times of elections.”
The spread of low-credibility content by social bots (2017, neutral): “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 ”
First Women, Second Sex: Gender Bias in Wikipedia (2015, background): “Bias on social media has been quantified in several ways, usually in topics like political polarization (Conover et al., 2011) and media bias (An et al.”
Birds of a Caste - How Caste Hierarchies Manifest in Retweet Behavior of Indian Politicians (2020, background): “Political speech online is generally polarized around “in group” and “out group” discourse [21] that usually makes party or partisanship the basis of grouping.”