ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, and Xiaoli Li
5 Pith papers cite this work. Polarity classification is still indexing.
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Disinformation detectors trained mainly on Standard American English show systematic F1 drops on 50 English dialects, with multilingual models far more robust than monolingual ones.
BOUTEF is a publicly available multilingual corpus for fake news research in Algeria and Tunisia, with narratives, comments, and debunkings across multiple languages and dialects, accompanied by thematic and engagement analyses.
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
BiMind outperforms existing methods in incorrect information detection by disentangling content and knowledge reasoning with attention geometry adaptation and self-retrieval.
citing papers explorer
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ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection
ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
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DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
Disinformation detectors trained mainly on Standard American English show systematic F1 drops on 50 English dialects, with multilingual models far more robust than monolingual ones.
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BOUTEF: A Multilingual Corpus for FakeNews in North Africa -- Language as a Weapon
BOUTEF is a publicly available multilingual corpus for fake news research in Algeria and Tunisia, with narratives, comments, and debunkings across multiple languages and dialects, accompanied by thematic and engagement analyses.
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection
BiMind outperforms existing methods in incorrect information detection by disentangling content and knowledge reasoning with attention geometry adaptation and self-retrieval.