The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.
By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang
arXiv:2606. 07651v1 Announce Type: new Abstract: Traditional fake news detection methods are falling behind as multimodal misinformation grows more advanced, seamlessly blending deceptive text, manipulated visuals, and factually incorrect claims.
By Kevin Patel, Shashi Bhushan Jha
Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limit...
The paper introduces MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths for large language models (LLMs) to reason about fake news propagation graphs. By compressing complex propagation structures into informative subgraphs, MAGER reduces modality mismatch and information overload, enabling frozen LLMs to perform structure-aware veracity reasoning. The authors also propose a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to enhance classification and reasoning, and report that MAGER significantly improves LLM performance in data‑efficient settings.
By Ziyi Zhou, Xiaoming Zhang, Hui Pang, Yuting Zhang, Tiesunlong Shen, Bingyu Yan, Erik Cambria, Litian Zhang
arXiv:2601.03981v3 Announce Type: replace
Abstract: To efficiently combat the spread of LLM-generated misinformation in the news domain, we present RADAR, a Retrieval-Augmented Detector with Adversar...
By Song-Duo Ma, Yi-Hung Liu, Hsin-Yu Lin, Pin-Yu Chen, Hong-Yan Huang, Shau-Yung Hsu, Yun-Nung Chen
arXiv:2608.22832v1 Announce Type: new
Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...
By Xiansheng Luo, Chaowei Zhang, Zewei Zhang, Yi Zhu, Jipeng Qiang
arXiv:2601. 14954v3 Announce Type: replace Abstract: Social media increasingly disseminates information through mixed image text posts, but rumors often exploit subtle inconsistencies and forged content, making detection based solely on post content difficult.
By Han Li, Hua Sun
arXiv:2608. 06732v1 Announce Type: new Abstract: Recent text-to-video (T2V) generation models enable fake news videos to be synthesized from scratch, shifting the threat beyond cheap fakes assembled from existing footage.
By Yifeng Luo, Yupeng Li, Liang Lan, Tian Wang
arXiv:2609.36902v1 Announce Type: new
Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level re...
By Wenbin Shen, Guoxuan Qin, Guangxu Yao, Baodong Wang, Yuanbo Rui, Zhongjie Ba, Zhichao Lian
arXiv:2609.20838v1 Announce Type: new
Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-en...
By Zeynep \"Ozdemir, Murat Osmano\u{g}lu, Sevgi Yi\u{g}it-Sert, \"Omer \"Ozg\"ur Tanr{\i}\"over, Y{\i}lmaz Ar
arXiv:2609.12678v1 Announce Type: new
Abstract: Short-video platforms have become a primary news source for the public, which has also enabled the widespread dissemination of fake news videos. We stu...
By Yifeng Luo, Yupeng Li, Ming Tang, Jianxiong Guo, Liang Lan
arXiv:2606. 03066v1 Announce Type: new Abstract: The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social stability.
By Jinjie Shen, Yaxiong Wang, Yujiao Wu, Lechao Cheng, Tianrui Hui, Nan Pu, Zhihui Li, Zhun Zhong