Papers by Yongchun Zhu
Zoom Out and Observe: News Environment Perception for Fake News Detection (2022.acl-long)
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| Challenge: | Existing methods for fake news detection "zoom in" to verify content with knowledge sources or check readers’ replies to posts but neglect information in the external news environment where a fake news post is created and disseminated. |
| Approach: | They propose a framework to capture news environment signals and a module to perceive useful signals and assist final prediction. |
| Outcome: | The proposed framework can improve the performance of basic fake news detectors by capturing the environmental signals of news posts and analyzing the results. |
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection (2023.acl-industry)
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| Challenge: | Existing work on fake news detection does not consider the temporal shift issue caused by the rapidly-evolving nature of news data. |
| Approach: | They propose a framework to forecast temporal patterns of news data and guide detector to fast adapt to future distributions. |
| Outcome: | The proposed framework forecasts temporal distribution patterns and guides detector to fast adapt to future distribution. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer (2022.coling-1)
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| Challenge: | Social media spreads both real news and fake news in various domains including politics, health, entertainment, etc. |
| Approach: | They propose a Domain- and Instance-level Transfer Framework for Fake News Detection which could improve the performance of specific target domains. |
| Outcome: | The proposed framework improves performance of target domains by hurting other domains, resulting in unsatisfactory performance in the target domain. |