[CIKM 2025] Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Zhengjia Wang, Qiang Sheng, Danding Wang, Beizhe Hu, and Juan Cao Proceedings of the 34th ACM International Conference on Information and Knowledge Management Preprint /
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Chinese Blog TL;DR: We propose to inject intent information with graph-based joint learning into fake news detection.
[CIKM 2025] Enhancing Fake News Video Detection via LLM-Driven Creative Process Simulation
Yuyan Bu, Qiang Sheng, Juan Cao, Shaofei Wang, Peng Qi, Yuhui Shi, and Beizhe Hu Proceedings of the 34th ACM International Conference on Information and Knowledge Management Preprint /
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Media Coverage: 52CV TL;DR: We propose AgentAug, which simulates the creative process of fake news videos using LLMs to mitigate the data scarcity issue.
[SIGIR 2025] LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation Beizhe Hu, Qiang Sheng, Juan Cao, Yang Li, Danding Wang Proceedings of The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval Preprint /
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Project Page TL;DR: We reveal the truth-decay phenomenon where real news gradually loses its top-ranked advantage against fake news when LLM-generated news penetrates.
2024
[CIKM 2024] Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models Qiong Nan, Qiang Sheng, Juan Cao,Beizhe Hu, Danding Wang, and Jintao Li Proceedings of the 33rd ACM International Conference on Information and Knowledge Management Preprint /
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GitHub Repo /
Chinese Blog TL;DR: We prompt LLMs to role-play social media users to obtain generated comments for enhancing fake news detection.
[IJCAI 2024]Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, and Danding Wang Proceedings of the 33rd International Joint Conference on Artificial Intelligence
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GitHub Repo /
Chinese Blog TL;DR: To detect and attribute text generated by black-box LMs, we estimate their generation probabilities of representative words guided by a white-box proxy LM to obtain a strong feature.
[AAAI 2024]Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi Proceedings of the 38th AAAI Conference on Artificial Intelligence Preprint /
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GitHub Repo /
English Video & Slides TL;DR: Large LMs generally underperform fine-tuned Small LMs for fake news detection, but they can be good advisors by providing rationales.
2023
[ACL 2023]Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection Beizhe Hu, Qiang Sheng, Juan Cao, Yongchun Zhu, Danding Wang, Zhengjia Wang, and Zhiwei Jin Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Preprint /
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Poster /
Slides TL;DR: We propose to address the temporal shift issue in real-world fake news detection systems via forecasting topic-level trends and accordingly adjusting the detector update strategy.