Dynamic Debates: An Analysis of Group Polarization Over Time on Twitter

By S. Yardi, D. boyd. 2010

599 citations recorded by the source corpus.

The principle of homophily says that people associate with other groups of people who are mostly like themselves. Many online communities are structured around groups of socially similar individuals. On Twitter, however, people are exposed to multiple, diverse points of view through the public timeline. The authors captured 30,000 tweets about the shooting of George Tiller, a late-term abortion doctor, and the subsequent conversations among pro-life and pro-choice advocates. They found that replies between like-minded individuals strengthen group identity, whereas replies between different-minded individuals reinforce in-group and out-group affiliation. Their results show that people are exposed to broader viewpoints than they were before but are limited in their ability to engage in meaningful discussion. They conclude with implications for different kinds of social participation on Twitter more generally.

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

View Paper: https://www.semanticscholar.org/paper/87b70acb6484656922f8b226ef774cb2256e1bf2

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Citations in the corpus

The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “…people’s exposure to ideologically diverse viewpoints (Grönlund, Herne, & Setälä, 2015; Mutz, 2002), existing data demonstrate that exposure to ideologically diverse view-points on social media can actually backfire— increasing political polarization (Bail et al., 2018; Yardi & Boyd, 2010).”
Political Polarization on Twitter (2011, background): “Finally, we provide evidence that these network structures result in part from politically motivated individuals annotating tweets with multiple hashtags whose primary audiences consist of ideologically-opposed users, a behavior also documented in the work of Yardi and boyd (2010).”
Learning Semantic Relationships between Entities in Twitter (2011, background): “Some questioned the usability of Twitter especially for novice users [19], studied capturing comprehensive locality information to be used for viewing tweets in the current local position of the mobile device [14], researched discovering and ranking fresh web sites using tweets [8], investigated the”
#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, background): “Consistent with previous findings [33, 49], we observed that a significant portion of in-group conversations were to show support for like-minded others, but out-group ones mainly expressed opposition, disapproval, or to question and provoke different-minded others, e.”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, result): “Other studies have also confirmed these results by demonstrating users’ higher willingness to communicate with other like-minded social media users [22, 37] and their inability to engage in meaningful discussions with differentminded users [47] .”
WeChat: social and political development of China’s dominant messaging app (2017, background): “In a study of Twitter, Yardi and Boyd (2010) found that like-minded replies reinforced group identity regarding specific issues.”
Assessing the bias in samples of large online networks (2014, methodology): “…sampling filter: queries run through the APIs sually request messages that contain certain hashtags (Borgeolthoefer et al., 2011; Gaffney, 2010; Yardi and boyd, 2010); or, nce a data set has been collected, keywords are used to idenify relevant streams and filter down larger data sets (Cha et…”
Predicting the Political Alignment of Twitter Users (2011, background): “‘Hashtags,’ the metadata feature on which we focus in this paper, are short tokens used to indicate the topic or intended audience of a tweet [5]; for example, #dadt for ‘Don’t Ask Don’t Tell’ or #jlot for ‘Jewish Libertarians on Twitter.”
Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior (2013, background): “In addition, replies among like-minded individuals would strengthen group identity, whereas replies among different-minded individuals would reinforce in-group and out-group affiliation [77].”
Assessing the Bias in Communication Networks Sampled from Twitter (2012, methodology): “…filter: queries run through the APIs usually request messages that contain some of those keywords (Borge-Holthoefer et al., 2011; Gaffney, 2010; Yardi and boyd, 2010); or, once a data set has been collected, keywords are used to identify relevant streams and filter 9 down larger data sets…”
Social media and political communication: a social media analytics framework (2012, background): “Similarly, Yardi and Boyd (2010) find that, in political”