Harnessing Partisan Motives to Combat Misinformation

About

Partisan motives are often conceptualized as fundamentally in opposition to accuracy-directed motives. Rather than being opposed, however, it may be that partisan and accuracy motivations simply operate independently – in which case political motives may not necessarily interfere with truth discernment. Here, we test this hypothesis in the context of crowd evaluations of (mis)information. We predict that in the presence of accuracy motivations, stronger partisan motivations can actually lead to better outcomes – an increased quantity of flags, coupled with as good or better truth discernment – by motivating people to preferentially flag news that is both false and politically discordant. To empirically assess this prediction, we carried out a survey study and analyzed field data from X’s (Twitter’s) crowdsourced fact-checking platform Community Notes. This data shows that more politically motivated individuals are more active community fact-checking participants, helping sustain overall contribution levels. Furthermore, our results show that more politically motivated participants engage in more politically biased flagging yet exhibit the same or better flagging discernment as compared to less politically motivated participants. Together, our results challenge the notion that partisan motives inherently undermine the ability and willingness to evaluate truth. Rather, political motivation may actually be the key to the provisioning of high quantity and quality crowdsourced fact-checks.

Cameron Martel is a fifth year PhD candidate in the Marketing Group at MIT Sloan School of Management, and will be starting as an Assistant Professor at Johns Hopkins Carey Business School in Fall 2025. His research investigates why people believe and share misinformation, what forces shape the online social networks through which misinformation may spread, and which content moderation interventions are effective for social media platforms to implement towards improving information quality online. He uses a variety of methods including online survey experiments, social media field experiments, behavioral economic games, and computational social science analytics. Cameron received his BS in cognitive science from Yale University, and his work is supported by the National Science Foundation Graduate Research Fellowship.