Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection

• URL: http://arxiv.org/abs/2512.21039v1
• Date: Wed, 24 Dec 2025 08:06:52 GMT
• Title: Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection
• Authors: Roopa Bukke, Soumya Pandey, Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak,
• Abstract summary: AMPEND-LS is an agentic multi-persona evidence-grounded framework for multimodal fake news detection.<n>It integrates textual, visual, and contextual signals through a structured reasoning pipeline powered by LLMs.<n>Experiments show that AMPEND-LS consistently outperformed state-of-the-art baselines in accuracy, F1 score, and robustness.<n>This work advances the development of adaptive, explainable, and evidence-aware systems for safeguarding online information integrity.
• Score: 0.7534418099163723
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of online misinformation 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 baselines in accuracy, F1 score, and robustness. Qualitative case studies further highlight its transparent reasoning and resilience against evolving misinformation. This work advances the development of adaptive, explainable, and evidence-aware systems for safeguarding online information integrity.

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