Exposure to opposing views on social media can increase political polarization

By C. Bail, Lisa P. Argyle, Taylor W. Brown, John P. Bumpus, Haohan Chen, M. F. Hunzaker, Jaemin Lee, M. Mann, Friedolin Merhout, Alexander Volfovsky. Proceedings of the National Academy of Sciences of the United States of America, 2018

1,554 citations recorded by the source corpus.

Significance Social media sites are often blamed for exacerbating political polarization by creating “echo chambers” that prevent people from being exposed to information that contradicts their preexisting beliefs. We conducted a field experiment that offered a large group of Democrats and Republicans financial compensation to follow bots that retweeted messages by elected officials and opinion leaders with opposing political views. Republican participants expressed substantially more conservative views after following a liberal Twitter bot, whereas Democrats’ attitudes became slightly more liberal after following a conservative Twitter bot—although this effect was not statistically significant. Despite several limitations, this study has important implications for the emerging field of computational social science and ongoing efforts to reduce political polarization online. 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.

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

View Paper: https://www.semanticscholar.org/paper/c1cb4725a7fb0df77c52d6d2792e87d588178715

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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): “Although some research would suggest that a solution to better civil discourse online would be to increase people’s exposure to ideologically diverse viewpoints (Grönlund, Herne, & Setälä, 2015; Mutz, 2002), existing data demonstrate that exposure to ideologically diverse viewpoints on social media ”
The COVID-19 social media infodemic (2020, background): “Indeed, users online tend to acquire information adhering to their worldviews [15], to ignore dissenting information [16, 17] and to form polarized groups around shared narratives [18, 19].”
Growing polarization around climate change on social media (2021, background): “F.Z. acknowledges financial support from the European Union’s Rights, Equality and Citizenship project EUMEPLAT grant no. 101004488.”
DEBAGREEMENT: A comment-reply dataset for (dis)agreement detection in online debates (2021, background): “Disagreements and polarisation Increased research efforts have been made to understand polarisation in social media [38, 3].”
A Transformer-based Framework for Neutralizing and Reversing the Political Polarity of News Articles (2021, background): “Partisans often selectively expose themselves to opposite political views that actually reinforces their pre-existing political beliefs [5, 80].”
The consequences of online partisan media (2021, background): “We follow recent research in looking for shifts in policy opinions across a range of issues (27).”
The future of social media in marketing (2019, background): “Notably, some research has found that exposure to opposing views (i.e., removing online echo chambers) may in fact increase (versus decrease) polarization (Bail et al.”
Measuring Stability and Change in Personal Culture Using Panel Data (2020, neutral): “…ideological commitments (Abramowitz and Saunders 2006; Baldassarri and Gelman 2008), or respond to exposure to opposing views by becoming more extreme in their opinions (Bail et al. 2018), the claim is rarely that these changes are temporary shocks that will revert to a stable baseline over time.”
Duality in Diversity: How Intrapersonal and Interpersonal Cultural Heterogeneity Relate to Firm Performance (2019, background): “Under some circumstances such exposure begets intrapersonal cultural breadth (Morris, Chiu, and Liu, 2015), but under others it serves to narrow cultural repertoires and entrench cultural divisions (Bail et al., 2018; Goldberg and Stein, 2018).”
Co-Writing with Opinionated Language Models Affects Users’ Views (2023, background): “Even without being designed to shift opinions, it has been found that algorithms may contribute to political polarization by reinforcing divisive opinions [9, 24, 32].”
Gaussian mixture models as a proxy for interacting language models (2025, unknown): “Furthermore, LLMs are computationally intensive, which translates to heavy energy, hardware, and time costs for training and text generation [3].”