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.

A growing body of research in sociology uses the concept of cultural schemas to explain how culture influences beliefs and actions. However, this work often relies on belief or attitude measures gleaned from survey data as indicators of schemas, failing to measure the cognitive associations that constitute schemas. In this article, we propose a concept-association-based approach for collecting data about individuals’ schematic associations, and a corresponding method for modeling concept network representations of shared cultural schemas. We use this method to examine differences between liberal and conservative schemas of poverty in the United States, uncovering patterns of associations expected based on previous research. Examining the structure of schematic associations provides novel insights to long-standing empirical questions regarding partisan attitudes toward poverty. Our method yields a clearer picture of what poverty means for liberals and conservatives, revealing how different concepts related to poverty indeed mean fundamentally different things for these two groups. Finally, we show that differences in schema structure are predictive of individuals’ policy preferences.

The negative outcomes associated with cultural stereotypes based on race, class, and gender and related schema-consistency biases are well documented. How these biases become culturally entrenched is less well understood. In particular, previous research has neglected the role of information transmission processes in perpetuating cultural biases. In this study, I combine insights from the cultural cognition, affect control theory, and cultural transmission frameworks to examine how one form of internalized culture—fundamental cultural sentiments—affects the content of information shared in communication. I argue that individuals communicate narratives in ways that minimize deflection of internalized cultural sentiments, resulting in cultural-consistency bias. I test this proposition using a serial transmission study in which participants read and retell short stories. Results show that culturally inconsistent, high-deflection information experiences an initial boost in memorability, but consistency biases ultimately win out as information is altered to increase cultural consistency, demonstrating that deflection provides a promising measure of cultural schema-consistency. This measure is predictive of the information that individuals share in communication and changes to this information in the transmission process.

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...

One of the most striking features of stereotypes is their extreme durability. This study focuses on the role played by cultural schemas and perceptions of low-status others’ adversities in stereotype perpetuation. Social psychological theories of legitimacy and justice point to the role of stereotypes as one means through which individuals make sense of others’ undeserved misfortunes by redefining the victim. This study connects this work with insights from cognitive cultural sociology to propose that stereotypes act as cultural schemas used to justify others’ experiences of adversity. Consistent with this hypothesis, findings from a cultural transmission experiment show that participants include more negative stereotype-consistent content when retelling narratives with undeserved negative outcomes than with positive outcomes. Cognitive cultural sociology and the cultural transmission methodology offer tools for understanding victim redefinition processes, with important implications for the reproduction of stereotype bias and social inequalities.