Designing algorithms against corruption a conjoint study on communicative features to encourage intentions for collective action

Algorithmic tools are increasingly used to automate corruption reporting on social media platforms. Based on the use case of an existing bot, this study investigates how to design the communication of a bot to effectively and responsibly mobilize people for collective action against corruption. In a pre-registered choice-based conjoint survey (n = 1,331), we test six message design features: type of injustice, degree of injustice, anger, political partisanship, gender, and efficacy cues. Our results show that calling out cases of severe corruption increased people’s intention to engage in collective action against corruption. We find no empirical support for in-group favoritism based on political affiliation and gender. Yet, some commonly used design features can have contrasting effects on different audiences. We call for more social science research accompanying the technical development of algorithmic tools to fight corruption.