@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://aclanthology.org/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."
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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.</abstract>
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%0 Conference Proceedings
%T Detecting AI-Generated Content on Social Media with Multi-modal Language Models
%A Yang, Chenyang
%A Yan, Shen
%A Yang, Yibo
%A Hu, Litao
%A Liu, Yuchen
%A Zeng, Yuan
%A Yu, Hanchao
%A Zhu, Yinan
%A Singla, Sumedha
%A Vanover, Brian
%A Qian, Huijun
%A Wang, Zihao
%A Liu, Fujun
%A Singh, Aashu
%A Wang, Jianyu
%A Zhang, Xuewen
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F yang-etal-2026-detecting
%X 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.
%U https://aclanthology.org/2026.acl-industry.15/
%P 217-229
Markdown (Informal)
[Detecting AI-Generated Content on Social Media with Multi-modal Language Models](https://aclanthology.org/2026.acl-industry.15/) (Yang et al., ACL 2026)
ACL
- 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, and Xuewen Zhang. 2026. Detecting AI-Generated Content on Social Media with Multi-modal Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 217–229, San Diego, California, USA. Association for Computational Linguistics.