Communication via social media is characterized by immediacy and anonymity, enabling free expression and sharing of opinions, but also the abuse of language in form of hate speech. Given the volume of online content, IS research offers approaches to efficiently detect hate speech. However, research and politics call for more independent, transparent, and social approaches to increase credibility and acceptance. In response, this two-part behavioral and neural study investigates flagging as a community-based solution to hate speech detection. By experimentally varying the displayed shares of flagging users and testing behavioral responses, results reveal opposing behavioral patterns as a function of the valuation of hate speech prevention. Moreover, by framing the display of the user community’s flagging behavior as a sort of social normative information and hate speech prevention as a public good, the theoretical model might help explain (seemingly) conflicting results in social norm and public goods research
