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This paper introduces a multi-branch feature fusion framework for detecting and propagating health misinformation, integrating semantic, rhetorical, and psychological cues to achieve strong performance on benchmark datasets.
This paper introduces RO-PnR, a decision framework that learns when to ask clarifying questions versus providing corrections in multi-turn health misinformation interventions, achieving higher cost-adjusted utility with fewer turns.
BERTopic-VP is a virality-prioritised topic-modelling framework that combines clustering with a misinformation detection module to analyze health misinformation on Twitter, achieving high classification performance for COVID-19 and Monkeypox outbreaks.
This paper proposes a culturally-sensitive responsible NLP framework for detecting health misinformation in low-resource languages like Bangla, evaluating small language models such as Phi-4 for claim extraction.