SignificanceSocial 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.

Do advocacy organizations stimulate public conversation about social problems by engaging in rational debate, or by appealing to emotions? We argue that rational and emotional styles of communication ebb and flow within public discussions about social problems due to the alternating influence of social contagion and saturation effects. These "cognitive-emotional currents" create an opportunity structure whereby advocacy organizations stimulate more conversation if they produce emotional messages after prolonged rational debate or vice versa. We test this hypothesis using automated text-analysis techniques that measure the frequency of cognitive and emotional language within two advocacy fields on Facebook over 1.5 years, and a web-based application that offered these organizations a complimentary audit of their social media outreach in return for sharing nonpublic data about themselves, their social media audiences, and the broader social context in which they interact. Time-series models reveal strong support for our hypothesis, controlling for 33 confounding factors measured by our Facebook application. We conclude by discussing the implications of our findings for future research on public deliberation, how social contagions relate to each other, and the emerging field of computational social science.

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 1 Twitter bot for one month that exposed them to messages produced by elected officials, organizations, and other opinion leaders with opposing political ideologies. Respondents were re-surveyed 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 post-treatment, and Democrats who followed a conservative Twitter bot became slightly more liberal post-treatment. These findings have important implications for the interdisciplinary literature on political polarization as well as the emerging field of computational social science.Political polarization in the United States has become a central focus of social scientists in recent decades (1-7). Americans remain deeply divided on controversial issues such as inequality, race, and immigration. According to the 2016 National Election Study, 59.3% of Clinton voters believe federal aid to the poor should be increased compared to only 20.2% of Trump voters. 77.7% of Clinton voters express favorable attitudes towards the Black Lives Matter movement, whereas 31.2% of Trump voters do the same. 68.9% of Trump voters believe immigration to the United States should be decreased, compared to 21.9% of Clinton voters.Longstanding divides about these and many other issues have far-reaching consequences for the design and implementation of social policies as well as the effective function of democracy more broadly (8-12).America's deep partisan divides are often attributed to "echo chambers," or patterns of information sharing that reinforce pre-existing political beliefs by limiting exposure to heterogeneous ideas and perspectives (13)(14)(15)(16)(17). Concern about selective exposure to information and political polarization has increased in the age of social media (13,(18)(19)(20). The vast majority of Americans now visit a social media site at least once each day, and a rapidly growing number 2 of them list social media as their primary source of news (21). Despite initial optimism that social media might enable people to consume more heterogeneous sources of information about current events, there is growing concern that such forums exacerbate political polarization because of social network homophily, or the well-documented tendency of people to form social network ties to those who are similar to themselves (22, 23). The endogenous relationship between social network formation and political attitudes also creates formidable challenges f...

A growing line of research underscores that sociodemographic factors may contribute to disparities in the impact of COVID-19. Further, stages of disease theory suggests that disparities may grow as the pandemic unfolds and more advantaged areas are better able to apply growing knowledge and mitigation strategies. In this paper, we focus on the role of county-level household overcrowding on disparities in COVID-19 mortality in U.S. counties. We examine this relationship across three theoretically important periods of the pandemic from April–October 2020, that mark both separate stages of community knowledge and national mortality levels. We find evidence that the percentage of overcrowded households is a stronger predictor of COVID-19 mortality during later periods of the pandemic. Moreover, despite a relationship between overcrowding and poverty at the county-level, overcrowding plays an independent role in predicting COVID-19 mortality. Our findings underscore that areas disadvantaged by overcrowding may be more vulnerable to the effects of COVID-19 and that this vulnerability may lead to changing disparities over time.

We investigated the effects of Facebook’s and Instagram’s feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users’ on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.