The Content Moderator’s Dilemma: Removal of Toxic Content and Distortions to Online Discourse

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ID: 329369
2026
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Abstract
Abstract There is an ongoing debate about how to moderate toxic speech on social media and the impact of content moderation on online discourse. This paper proposes and validates a methodology for measuring the content-moderation-induced distortions in online discourse using text embeddings from computational linguistics. Applying the method to a representative sample of 5 million US political Tweets, we find that removing toxic Tweets significantly alters the semantic composition of content. The magnitudes of the distortions are comparable to removing 4 out of 67 topics from the online discourse at random. This finding is consistent across different embedding models, toxicity metrics, and samples. Importantly, we demonstrate that these effects are not solely driven by toxic language but by the removal of topics often expressed in toxic form. We propose an alternative approach to content moderation that uses generative Large Language Models to rephrase toxic Tweets, preserving their salvageable content rather than removing them entirely. We show that this rephrasing strategy reduces toxicity while mitigating distortions in online content.
Reference Key
openalex_W7213972764 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mahyar Habibi, Carlo Schwarz
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Journal the economic journal
Year 2026
DOI
10.1093/ej/ueag115
URL
https://doi.org/10.1093/ej/ueag115
Keywords Keywords not found

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