Simulating Hate Speech Cascades with Multi-LLM Agents: Empirical Grounding, Modeling Fidelity, and Intervention Strategies
arXiv:2606.18264v1 Announce Type: cross Abstract: Faithful modeling of hateful content propagation on online platforms remains an open problem for moderation research. Classical cascade models that do not explicitly represent the profile, community, and content factors associated with hateful-content propagation may yield moderation strategies that behave less effectively when deployed in real-world scenarios. Multi-agent large language model (LLM) systems can, in principle, make each reshare decision depend on the user's profile, the surrounding community, and the post's content, but it remai
The proliferation of generative AI and large language models necessitates more sophisticated approaches to understand and combat harmful content online.
Accurate modeling of hate speech propagation is crucial for developing effective moderation policies and maintaining healthy online discourse, directly impacting social stability and platform viability.
The use of multi-LLM agents allows for more nuanced and realistic simulation of online social dynamics, moving beyond classical cascade models to consider user profiles and community context.
- · Social media platforms
- · Content moderation tech providers
- · AI ethics researchers
- · Law enforcement/intelligence agencies
- · Hate speech propagators
- · Platforms with ineffective moderation
- · Early, less sophisticated moderation models
Improved understanding and prediction of hate speech cascades on online platforms.
Development of more effective, proactive moderation tools and intervention strategies.
Reduction in the spread and impact of harmful content, leading to more resilient and civil online communities.
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Read at arXiv cs.AI