Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916017151672320 |
|---|---|
| author | Huang, Jen-tse Chen, Chang Lai, Shiyang Wang, Wenxuan Kaufman, Michelle R. Dredze, Mark |
| author_facet | Huang, Jen-tse Chen, Chang Lai, Shiyang Wang, Wenxuan Kaufman, Michelle R. Dredze, Mark |
| contents | Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns-experimental errors, logical fallacies, and fabricated claims-each verified by evidence such as national standards and academic literature. We evaluate eight frontier MLLMs across five modality settings. Experimental results demonstrate that Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Furthermore, we investigate social cues that induce false beliefs in videos and find that models are susceptible to biases like authoritative channel IDs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06600 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation Huang, Jen-tse Chen, Chang Lai, Shiyang Wang, Wenxuan Kaufman, Michelle R. Dredze, Mark Computation and Language Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language Models (MLLMs) have demonstrated impressive reasoning capabilities, their robustness against misinformation entangled with cognitive biases remains under-explored. In this paper, we introduce a comprehensive evaluation framework using a high-quality, manually annotated dataset of 200 short videos spanning four health domains. This dataset provides fine-grained annotations for three deceptive patterns-experimental errors, logical fallacies, and fabricated claims-each verified by evidence such as national standards and academic literature. We evaluate eight frontier MLLMs across five modality settings. Experimental results demonstrate that Gemini-2.5-Pro achieves the highest performance in the multimodal setting with a belief score of 71.5/100, while o3 performs the worst at 35.2. Furthermore, we investigate social cues that induce false beliefs in videos and find that models are susceptible to biases like authoritative channel IDs. |
| title | Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.06600 |