Exposure to opposing views on social media can increase political polarization.

Abstract

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1073/pnas.1804840115

Publication Info

Bail, Christopher A, Lisa P Argyle, Taylor W Brown, John P Bumpus, Haohan Chen, MB Fallin Hunzaker, Jaemin Lee, Marcus Mann, et al. (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.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Bail

Christopher Andrew Bail

Professor of Sociology

Chris Bail is Professor of Sociology, Computer Science, Political Science, and Public Policy at Duke University, where he directs the Society-Centered AI Initiative and co-founded the Polarization Lab. He studies how artificial intelligence shapes human behavior in a range of different settings—and social media platforms in particular.

 A Guggenheim Fellow and Carnegie Fellow, Chris's writing appears in leading outlets such as Science,  Nature,  the New York Times, and Harvard Business Review. His widely acclaimed 2021 book, Breaking the Social Media Prism, was featured in the New York Times, the New Yorker, and described as “masterful,” by Science Magazine. It also inspired Twitter to implement a major change to its policies designed to counter misinformation and polarization. His co-authored research on using generative AI for conflict mediation also inspired a new product on NextDoor. 

Bail has also written for the Sunday Op-Ed page of the New York Times, CNN, and The Washington Post Blog and appeared on NBC Nightly News, CBS, CNN, BBC, and NPR to discuss his research. His work has been covered by more than sixty media outlets, including The New York Times, The New Yorker, Time Magazine, The Wall Street Journal, Wired, The Atlantic, Scientific American, Foreign Policy, The Washington Post, The Los Angeles Times, The Boston Globe, The Guardian, Vox, Daily Kos, National Public Radio, NBC News, C-Span, and the BBC. 

​Chris is passionate about building the field of computational social science. He is the Editor of the Oxford University Press Series in Computational Social Science and the Co-Founder of the Summer Institutes in Computational Social Science, which are free training events designed to introduce junior scholars to the field that are held concurrently in a range of universities around the world each year. He also serves on the Advisory Committee to the National Science Foundation's Social Behavioral and Economic Sciences Directorate, and helped create Duke's Interdisciplinary Data Science Program. After the publication of his 2021 book, Chris began consulting with social media companies, non-profit groups, and governments to implement insights from his research.

​Most of the funding for Bail's research has been provided by the National Science Foundation, the Carnegie Foundation, the Guggenheim Foundation, the Robert Wood Johnson Foundation, and the Russell Sage Foundation, among others described on the C.V. linked on this site. Chris received his PhD from Harvard University in 2011.

Volfovsky

Alexander Volfovsky

Associate 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 research is concerned with developing theory and methodological tools for approaching such modern data structures by better understanding these underlying dependence structures. My work concentrates on better understanding Kronecker covariance structures as they are related to network analysis and high dimensional unbalanced factorial designs. I work on theory and methodology for high dimensional data as it relates to network analysis, causal inference and computational and statistical tradeoffs. My primary applied interest is in the health and social sciences with past and ongoing collaborations studying friendship formation in high schools, employment outcomes for college graduates and job mobility as a function of an underlying social network.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.