[2512.21039] Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection

Computer Science > Information Retrieval arXiv:2512.21039 (cs) [Submitted on 24 Dec 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection Authors:Roopa Bukke, Soumya Pandey, Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak View a PDF of the paper titled Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection, by Roopa Bukke and 4 other authors View PDF HTML (experimental) Abstract:The rapid proliferation of online misinformation threatens the stability of digital social systems and poses significant risks to public trust, policy, and safety, necessitating reliable automated fake news detection. Existing methods often struggle with multimodal content, domain generalization, and explainability. We propose AMPEND-LS, an agentic multi-persona evidence-grounded framework with LLM-SLM synergy for multimodal fake news detection. AMPEND-LS integrates textual, visual, and contextual signals through a structured reasoning pipeline powered by LLMs, augmented with reverse image search, knowledge graph paths, and persuasion strategy analysis. To improve reliability, we introduce a credibility fusion mechanism combining semantic similarity, domain trustworthiness, and temporal context, and a complementary SLM classifier to mitigate LLM uncertainty and hallucinations. Extensive experiments across three benchmark datasets demonstrate that AMPEND-LS consistently outperformed state-of-the-art base...