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arXiv · 2211.16525

Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support

Charlotte Schluger·Jonathan P. Chang·Cristian Danescu-Niculescu-Mizil·Karen Levy
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Abstract

To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. This reactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms -- such as proactively discouraging the emergence of antisocial behavior rather than reacting to it -- and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow.

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BibTeXRIS

Charlotte Schluger, Jonathan P. Chang, Cristian Danescu-Niculescu-Mizil, Karen Levy. 2022-11-29. Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support. https://doi.org/10.1145/3555095

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