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cs.CL2025★ 1 cited
DiFaR: Enhancing Multimodal Misinformation Detection with Diverse, Factual, and Relevant Rationales
Herun Wan, Jiaying Wu, Minnan Luo +3
Generating textual rationales from large vision-language models (LVLMs) to support trainable multimodal misinformation detectors has emerged as a promising paradigm. However, its e…
cs.CL2024
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
Hao Guo, Zihan Ma, Zhi Zeng +4
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake n…