Abstract
Current online moderation follows a one-size-fits-all approach, where each intervention is applied in the same way to all users. This naïve approach is challenged by established socio-behavioral theories and by recent empirical results that showed the limited effectiveness of such interventions. We propose a paradigm-shift in online moderation by moving towards a personalized and user-centered approach. Our multidisciplinary vision combines state-of-the-art theories and practices in diverse fields such as computer science, sociology and psychology, to design personalized moderation interventions (PMIs). In outlining the path leading to the next-generation of moderation interventions, we also discuss the most prominent challenges introduced by such a disruptive change.
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CITATION STYLE
Cresci, S., Trujillo, A., & Fagni, T. (2022). Personalized Interventions for Online Moderation. In HT 2022: 33rd ACM Conference on Hypertext and Social Media - Co-located with ACM WebSci 2022 and ACM UMAP 2022 (pp. 248–251). Association for Computing Machinery, Inc. https://doi.org/10.1145/3511095.3536369
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