Marcus Mann is an assistant professor of sociology at Purdue University. His overarching interest is in how different and sometimes conflicting cultural knowledge authorities affect individuals’ perceptions of science, politics, religion, and reality more generally. He has studied this general question in the context of atheist social movements, polarization in political media, and attitudes toward science and scientists. Currently he is working on several projects related to political media diets and susceptibility to political disinformation.
PhD in Sociology, 2019
MA in Sociology, 2017
MA in Religious Studies, 2013
BA in English Studies, 2008
University of Massachusetts - Amherst
Abstract. The decline in trust in the scientific community in the United States among political conservatives has been well established. But this observation i
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.
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.
Between 2014 and 2016, the rate of homicide and other violent crime in the United States rose. One hypothesis discussed in the press and by some social scientists is that this increase was tied to political mobilization against police violence: As the Black Lives Matter movement gained support following protests in Ferguson, Missouri, perhaps police officers, worried about the new public mood, scaled back their law enforcement efforts, with crime as a consequence. In this article, we examine the association between public concern over police violence and crime rates using Google search measures to estimate the former. Analyzing data on 43 large U.S. cities, we find that violent crime was higher and rose more in cities where concern about police violence was greatest. We also find that measures of social inequality predict crime rates. We conclude by discussing the implications for future research on the “Ferguson effect” and beyond.