WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
Ruichao Yang, Wei Gao, Jing Ma, Hongzhan Lin, Zhiwei Yang
Abstract
In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile, mostly controversial stance expressions among social media users. Rumor verification and stance detection are different yet relevant tasks. Fake news debunking primarily focuses on determining the truthfulness of news articles, which oversimplifies the issue as fake news often combines elements of both truth and falsehood. Thus, it becomes crucial to identify specific instances of misinformation within the articles. In this research, we investigate a novel task in the field of fake news debunking, which involves detecting sentence-level misinformation. One of the major challenges in this task is the absence of a training dataset with sentence-level annotations regarding veracity. Inspired by the Multiple Instance Learning (MIL) approach, we propose a model called Weakly Supervised Detection of Misinforming Sentences (WSDMS). This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences. We evaluate WSDMS on three real-world benchmarks and demonstrate that it outperforms existing stateof-the-art baselines in debunking fake news at both the sentence and article levels. News Title: NASA Will Pay You 100,000 USD To Stay In Bed For 60 Days! News Article: 𝑠 ! : Wouldn't you just love to carry on sleeping on a Monday morning without having to submit to the Monday morning blues and get ready for work? 𝑠 " : What type of heaven would you envisage if you were paid to stay in bed 𝑠 # : You can get paid a huge sum of money just staying in bed for two whole months and by you know who, NASA no less!!! yes the American space agency NASA is paying : Most of us dream about hanging out in bed, all day, every day. 𝑠 % : NASA is currently on the lookout for people to participate in their "Bed Rest Studies", in which participants will have to stay in bed for 60 days straight. 𝑠 & : It does sound like the dream job, right? … 𝑠 ' : You wouldn't just be sleeping you can keep yourself occupied with books, TV, video games, and they can also use their phones as they please… Only 100,000? Not good enough. … 𝑡 ! 𝑡 " 𝑡 # 𝑡 Fact Check: Does a 'NASA Study...Pay You 18,000 to stay in bed for 70 days straight… Sis go for it!! Can you eat among these days?? I'm pretty sure they provide food and water… negative impact it … Oh I need money, I can stay in bed for 70 days! Girl me too. im down for this. @NASA hmu … Fact Check: Does a 'NASA Study...Pay You 100,000? Not good.
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Cited by top-tier papers3
- Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation DetectionHerun Wan, Jiaying Wu, Minnan Luo, Zhi Zeng et al.NeurIPS 2025 · 14 citations
- On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMsHerun Wan, Minnan Luo, Zhixiong Su, Guang Dai et al.ACL 2025 · 5 citations
- Probabilistic Concept Graph Reasoning for Multimodal Misinformation DetectionRuichao Yang, Wei Gao, Xiaobin Zhu, Jing Ma et al.CVPR 2026 · 1 citation
Builds on18
- Rumor Detection on Social Media with Bi-Directional Graph Convolutional NetworksTian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao et al.AAAI 2020 · 773 citations
- GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social MediaYi-Ju Lu, Cheng-Te LiACL 2020 · 387 citations
- Cross-modal Ambiguity Learning for Multimodal Fake News DetectionYixuan Chen, Dongsheng Li, Peng Zhang, Jie Sui et al.WWW 2022 · 325 citations
- Interpretable Rumor Detection in Microblogs by Attending to User InteractionsLing Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing JiangAAAI 2020 · 231 citations
- Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal DataAmila Silva, Ling Luo, Shanika Karunasekera, Christopher LeckieAAAI 2021 · 170 citations
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