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.

Keywords: computational social science; political polarization; social media; social networks; sociology.

Fig. 1.

Overview of research design.

Fig. 2.

Design of study’s Twitter bots.

Fig. 3.

Effect of following Twitter bots that retweet messages by elected officials, organizations, and opinion leaders with opposing political ideologies for 1 mo, on a seven-point liberal/conservative scale where larger values indicate more conservative opinions about social policy issues, for experiments with Democrats (n = 697) and Republicans (n = 542). Models predict posttreatment liberal/conservative scale score and control for pretreatment score on this scale as well as 12 other covariates described in SI Appendix. Circles describe unstandardized point estimates, and bars describe 90% and 95% confidence intervals. “Respondents Assigned to Treatment” describes the ITT effect for Democrats (ITT = −0.02, $t$ = −0.76, $p$ = 0.45, $n$ = 416) and Republicans (ITT = 0.12, $t$ = 2.68, $p$ = 0.008, $n$ = 316). “Minimally-Compliant Respondents” describes the CACE for respondents who followed one of the study’s bots for Democrats (CACE = −0.04, $t$ = −0.75, $p$ = 0.45, $n$ of compliant respondents = 271) and Republicans (CACE = 0.19, $t$ = 2.73, $p<$ 0.007, $n$ of compliant respondents = 181). “Partially-Compliant Respondents” describes the CACE for respondents who correctly answered at least one question, but not all questions, about the content of a bot’s tweets during weekly surveys throughout the study period for Democrats (CACE = −0.05, $t$ = −0.75, $p$ = 0.45, $n$ of compliant respondents = 211) and Republicans (CACE = 0.31, $t$ = 2.73, $p<$.007, $n$ of compliant respondents = 121). “Fully-Compliant Respondents” describes the CACE for respondents who answered all questions about the content of the bot’s tweets correctly for Democrats (CACE = −0.14, $t$ = −0.75, $p$ = 0.46, $n$ of compliant respondents = 66) and Republicans (CACE = 0.60, $t$ = 2.53, $p<$ 0.01, $n$ of compliant respondents = 53). Although treated Democrats exhibited slightly more liberal attitudes posttreatment that increase in size with level of compliance, none of these effects were statistically significant. In contrast, treated Republicans exhibited substantially more conservative views posttreatment that increase in size with level of compliance, and these effects are highly significant.