@inproceedings{yang-etal-2026-detecting,
    title = "Detecting {AI}-Generated Content on Social Media with Multi-modal Language Models",
    author = "Yang, Chenyang  and
      Yan, Shen  and
      Yang, Yibo  and
      Hu, Litao  and
      Liu, Yuchen  and
      Zeng, Yuan  and
      Yu, Hanchao  and
      Zhu, Yinan  and
      Singla, Sumedha  and
      Vanover, Brian  and
      Qian, Huijun  and
      Wang, Zihao  and
      Liu, Fujun  and
      Singh, Aashu  and
      Wang, Jianyu  and
      Zhang, Xuewen",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.15/",
    pages = "217--229",
    ISBN = "979-8-89176-394-4",
    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."
}