Figure 4: An example failure case in multimodal fake-news detection.To assess whether MLLMs make fake-news decisions in a human-like manner, we conduct an additional human annotation study on 300 news items, sampling 100 items from each dataset with a balanced split of 50 true and 50 fake items. Following the same structure as CoT models, annotators first judge four verification dimensions—factual accuracy of text, misleading language in text, image–text alignment, and image authenticity—and then provide a final veracity judgment (true/fake). These dimensions serve as diagnostic cues for analyzing the reasoning process rather than as independent definitions of news veracity. In particular, image authenticity must be considered jointly with textual factuality and cross-modal evidence, since legitimate news may contain AI-generated illustrations, while fake news may reuse authentic but out-of-context images. The annotation protocol is described in Appendix [C](https://arxiv.org/html/#A3 "Appendix C Human Annotation ‣ Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?"), and the interface is shown in Fig. [6](https://arxiv.org/html/#A3.F6 "Figure 6 ‣ Appendix C Human Annotation ‣ Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?"). 出处: Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?（arXiv）
Figure 2: An agent framework for generating multimodal social media fake newsIn this paper, we propose a multi-agent framework to generate multimodal social media fake news grounded in real-world events. As shown in Fig. [1](https://arxiv.org/html/#S0.F1 "Figure 1 ‣ Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?"), the fake news post generated by our framework comprises a short story and a corresponding image that counter the true news narrative. For example, given a Nature news article about the Rosetta mission, our framework reverts the successful deliberate ”soft crash into Comet 67P” into a fabricated failure story of an ”unplanned collision”, while retaining key factual anchors such as ESA, Rosetta, and Comet 67P to ensure narrative plausibility and deceive readers. 出处: Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?（arXiv）
