AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments

• URL: http://arxiv.org/abs/2507.08110v1
• Date: Thu, 10 Jul 2025 18:52:50 GMT
• Title: AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments
• Authors: Saeedeh Mohammadi, Taha Yasseri,
• Abstract summary: This study explores an AI-assisted hybrid moderation framework in which participants receive AI-generated feedback on their notes.<n>The results show that incorporating feedback improves the quality of notes, with the most substantial gains resulting from argumentative feedback.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today, social media platforms are significant sources of news and political communication, but their role in spreading misinformation has raised significant concerns. In response, these platforms have implemented various content moderation strategies. One such method, Community Notes on X, relies on crowdsourced fact-checking and has gained traction, though it faces challenges such as partisan bias and delays in verification. This study explores an AI-assisted hybrid moderation framework in which participants receive AI-generated feedback -supportive, neutral, or argumentative -on their notes and are asked to revise them accordingly. The results show that incorporating feedback improves the quality of notes, with the most substantial gains resulting from argumentative feedback. This underscores the value of diverse perspectives and direct engagement in human-AI collective intelligence. The research contributes to ongoing discussions about AI's role in political content moderation, highlighting the potential of generative AI and the importance of informed design.

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