Detecting AI-Generated Content on Social Media with Multi-modal Language Models
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Computer Science > Computation and Language
arXiv:2606.11200 (cs)
[Submitted on 21 Apr 2026]
Title:Detecting AI-Generated Content on Social Media with Multi-modal Language Models
Authors:Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
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Abstract:Generative AI has enabled the creation of photorealistic images and videos that are increasingly disseminated on social media, often used for spam, misinformation, manipulation, and fraud. Existing AI-generated content (AIGC) detection methods face challenges including poor generalization to new generation models, reliance on single modalities, and lack of interpretable explanations. We present our pipeline that mitigates these issues by continuously curating diverse multi-modal social media data and training a compact vision-language model for detection and explanation. Our model achieves state-of-the-art detection performance on public benchmarks and demonstrates robust detection and explanation capabilities on internal social media datasets across multiple platforms. We deployed our model for post recommendation on social media platforms and observed positive downstream impacts on user engagement, demonstrating that it is feasible to perform effective AIGC detection in dynamic, real-world social media environments.
| Subjects: | Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.11200 [cs.CL] |
| (or arXiv:2606.11200v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.11200
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Detecting AI-Generated Content on Social Media with Multi-modal Language Models, by Chenyang Yang and 15 other authors
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