arXiv:2606.18268v1 Announce Type: cross Abstract: Community-based fact-checking that relies on cross-consensus is expanding rapidly on social media platforms. However, the delay and low-ratio of cross-consensus community fact-checks rated by human contributors remains a significant challenge. To address this, we first created ComRate, a large-scale dataset comprising 2.5 million community notes and over 209 million ratings sourced from $\mathbb{X}$. We then propose MultiCom, a persona-guided multi-agent rating framework for community note evaluation. MultiCom simulates diverse rater population
The rapid expansion of social media and community-based fact-checking necessitates more efficient and scalable evaluation methods as human efforts struggle to keep pace with information volume.
This development indicates a significant advancement in leveraging AI to enhance content moderation and fact-checking at scale, directly impacting the integrity of information on social media platforms.
The introduction of a multi-agent simulation framework offers a potential solution to the critical challenge of slow and low-ratio human-based community note evaluation, enabling more rapid and comprehensive automated assessments.
- · Social Media Platforms
- · AI/ML Developers
- · Users Seeking Verified Information
- · Misinformation Propagators
- · Manual Fact-Checking Organizations (if not adaptable)
AI-powered systems will become a primary component in evaluating and distributing content authenticity on large social media platforms.
The improved efficiency of fact-checking could lead to greater public trust in information disseminated via social media, or conversely, new forms of adversarial AI attacks to bypass these systems.
This could set a precedent for autonomous AI agents taking on increasingly complex and subjective moderation roles, potentially reshaping the landscape of online discourse and platform governance.
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