Asymmetrical Perceptions of Partisan Political Bots Institutional Repository Document

Political bots are social media algorithms that impersonate political actors and interact with other users, aiming to influence public opinion. This study examines perceptions of bots with partisan personas by conducting an online experiment (N = 656) that examines the ability to differentiate bots from humans on Twitter. We explore how characteristics of the experiment participants and the profiles being evaluated bias recognition accuracy. Our analysis reveals asymmetrical partisan-motivated reasoning, in that conservative profiles appear to be more confusing and Republican participants perform less well in the recognition task. Moreover, Republican users are more likely to confuse conservative bots with humans, whereas Democratic users are more likely to confuse conservative human users with bots. We discuss implications for how partisan identities affect motivated reasoning and how political bots exacerbate political polarization.