Fatima Alqabandi

Research on the impacts of online political discussions have focused on social media "echo chambers," but less is known about how people respond to online environments dominated by those who are politically dissimilar. We conduct a preregistered experiment using a mobile application we developed to evaluate how being outnumbered by out-partisans impacts comfort with sharing opinions as well as perceptions of the platform and its users. Our app mimics a social media platform but provides researcher control over platform features to experimentally isolate their effects and uses automated chatbots to create a dynamic newsfeed for the participant. We find that engaging on a platform dominated by out-partisans reduces comfort with sharing one’s opinions and lowers evaluations of the platform and its users. These findings shed light on a relatively less explored online environment (i.e., an outnumbered setting) and highlight the utility of LLMs in social science research.

Recent surveys indicate most Americans are reluctant to share their political views on social media. This study examines how social identity and group composition shape self-censorship in a simulated social media setting. I recruited Democrats and Republicans (N=758) to complete a survey about their political views and randomized them into to chats to discuss a politically contentious issue with members of the other party or members from their own party. I find Democrats are more likely to disclose their opinions to Republicans than to fellow Democrats. Yet Republicans were no more likely to express their opinions to Democrats or Republicans. This research underscores the significance of political identity dynamics on the expression of minority views and uses a behavioral measure of self-censorship which may improve upon previous self-reported measures of self-censorship. My study thus contributes to ongoing debates with political sociology, social psychology, and the emerging field of computational social science.

There is mounting concern about the prevalence of toxic political content on social media. Policy makers and tech leaders have called for platforms to provide users with the choice to avoid such content via algorithmic intervention, but little is known about how such reforms would be received by social media users. We present two survey experiments on a simulated social media platform to examine how choice shapes user’s attitudes about content as well as the platforms themselves. We find that most users elect to avoid such content— and that being offered this choice improves evaluations of the platform. However, offering this choice either has no effects on their attitudes about platform content, or makes them rate the content they view more negatively. Our findings have important implications for future research on political polarization, social psychology, and the growing field of computational social science.

Women have less influence than men in a variety of settings. Does this result from stereotypes that depict women as less capable, or biased interpretations of gender differences in behavior? We present a field experiment that—unbeknownst to the participants—randomized the gender of avatars assigned to Democrats using a social media platform we created to facilitate discussion about the 2020 Primary Election. We find that misrepresenting a man as a woman undermines his influence, but misrepresenting a woman as a man does not increase hers. We demonstrate that men’s higher resistance to being influenced—and gendered word use patterns—both contribute to this outcome. These findings challenge prevailing wisdom that women simply need to behave more like men to overcome gender discrimination and suggest that narrowing the gap will require simultaneous attention to the behavior of people who identify as women and as men.