Exposure to opposing views on social media can increase political polarization.
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There is mounting concern that social media sites contribute to political polarization by creating "echo chambers" that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.
Published Version (Please cite this version)10.1073/pnas.1804840115
Publication InfoBail, Christopher; Volfovsky, Alexander; Argyle, Lisa P; Brown, Taylor W; Bumpus, John P; Chen, Haohan; ... Merhout, Friedolin (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences of the United States of America, 115(37). pp. 9216-9221. 10.1073/pnas.1804840115. Retrieved from https://hdl.handle.net/10161/17683.
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Professor of Sociology
Chris Bail is Professor of Sociology, Public Policy, and Data Science at Duke University, where he directs the Polarization Lab. A leader in the emerging field of computational social science, Bail’s research examines fundamental questions of social psychology, extremism, and political polarization using social media data, bots, and the latest advances in machine learning. Bail is the recipient of Guggenheim and Carnegie Fellowships. His research appears in top journal
Assistant Professor of Statistical Science
I am interested in theory and methodology for network analysis, causal inference and statistical/computational tradeoffs and in applications in the social sciences. Modern data streams frequently do not follow the traditional paradigms of n independent observations on p quantities of interest. They can include complex dependencies among the observations (e.g. interference in the study of causal effects) or among the quantities of interest (e.g. probabilities of edge formation in a network). My r
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