The Future of Combating Rumors? Retrieval, Discrimination, and Generation
This addresses the societal, economic, and political impacts of AI-generated rumors, offering a more effective solution than current classification-based detection methods.
The paper tackles the problem of rumor detection by proposing a comprehensive debunking process that not only detects rumors but also generates explanatory content to refute misinformation, achieving satisfactory discrimination and explanatory effects while saving computational costs by eliminating the need for fine-tuning.
Artificial Intelligence Generated Content (AIGC) technology development has facilitated the creation of rumors with misinformation, impacting societal, economic, and political ecosystems, challenging democracy. Current rumor detection efforts fall short by merely labeling potentially misinformation (classification task), inadequately addressing the issue, and it is unrealistic to have authoritative institutions debunk every piece of information on social media. Our proposed comprehensive debunking process not only detects rumors but also provides explanatory generated content to refute the authenticity of the information. The Expert-Citizen Collective Wisdom (ECCW) module we designed aensures high-precision assessment of the credibility of information and the retrieval module is responsible for retrieving relevant knowledge from a Real-time updated debunking database based on information keywords. By using prompt engineering techniques, we feed results and knowledge into a LLM (Large Language Model), achieving satisfactory discrimination and explanatory effects while eliminating the need for fine-tuning, saving computational costs, and contributing to debunking efforts.