AI Feedback Enhances Community-Based Content Moderation through Engagement with Counterarguments
For social media platforms using community-based content moderation, this work demonstrates that AI-generated counterarguments can enhance note quality, addressing challenges like partisan bias and verification delays.
This study proposes an AI-assisted hybrid moderation framework where participants receive AI-generated feedback (supportive, neutral, or argumentative) on their community notes and revise them. Results show that incorporating feedback improves note quality, with argumentative feedback yielding the most substantial gains.
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 (formerly Birdwatch) on X (formerly Twitter), relies on crowdsourced fact-checking and has gained traction. However, 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 note quality, with the most substantial gains coming 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.