arXiv AI
Sep 30

Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

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 Machine Learning
Jun 16

YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories

arXiv:2606. 14780v1 Announce Type: cross Abstract: Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets.

By Md. Minhazul Islam, Md. Tanbeer Jubaer, Amith Khandakar, Shovon Sarker, Sumaiya Rahman, Md. Masum Mia, Mohamed Arselene Ayari, Hamed Noori