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This repo is a collection of AWESOME things about fake news detection, including papers, code, etc.
| Date | Stars |
|---|---|
| 2026-07-24 | 254 |
| 2026-07-25 | 254 |
| 2026-07-28 | 254 |
| 2026-07-30 | 254 |
| 2026-08-06 | 254 |
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# Fake News Detection 虚假新闻检测
[](https://opensource.org/licenses/MIT)
This repo is a collection of AWESOME things about fake news detection, including papers, code, etc. Feel free to star and fork.
---
## Contents
- [Papers](#paper)
- [Survey](#survey) 综述
- [Social Context](#social) 社交上下文
- [News Contents](#content-base) 新闻内容
- [Multi-Modal](#multi-modal) 多模态
- [Emotion](#emotion) 情感
- [Style](#style) 风格
- [Discourse Stucture](#discourse) 语篇结构
- [Fact Checking](#fact) 真实性检验
- [Explainable](#explainable) 可解释
- [Transfer Learning](#transfer) 迁移学习
- [Datasets](#datasets)
- [Distinguished Scholars in Fake News Detection](#scholars)
---
## <span id="paper">Papers</span>
### <span id="survey">Survey</span> 综述
- [A survey on fake news and rumour detection techniques](https://www.sciencedirect.com/science/article/pii/S0020025519304372). Information Sciences, 2019, 497: 38-55.
- [Detection and resolution of rumours in social media: A survey](https://dl.acm.org/doi/abs/10.1145/3161603). ACM Computing Surveys (CSUR), 2018, 51(2): 1-36.
- [The Spread of True and False News Online](https://science.sciencemag.org/CONTENT/359/6380/1146.abstract). Science, 2018, 359(6380): 1146-1151.
- [Fake News Detection on Social Media: A Data Mining Perspective](https://dl.acm.org/doi/abs/10.1145/3137597.3137600?casa_token=Mf0tvofQf7kAAAAA:LgdXVmsJzYxVyrTgrhoFio_zxDXORoh6NNGP4__D64yam0rOKfwdbi__38Jg01U7pC-M19Tkb2NC_BU). ACM SIGKDD explorations newsletter, 2017, 19(1): 22-36.
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### <span id="social">Social Context</span> 社交上下文
- KDD-2021 [Causal Understanding of Fake News Dissemination on Social Media](http://www.cs.iit.edu/~kshu/files/kdd_causal.pdf)
- To mitigate negative impact of fake news, this paper argued that it is critical to understand what user attributes potentially cause users to share fake news.
- 为了减轻虚假新闻的负面影响，本文认为了解哪些用户属性可能导致用户分享假新闻至关重要。
- SIGIR-2021 [User Preference-aware Fake News Detection](https://arxiv.org/pdf/2104.12259) [code](https://github.com/safe-graph/GNN-FakeNews)
- A user is more likely to spread a piece of fake news when it confirms his/her existing beliefs/preferences. This paper studied the novel problem of exploiting user preference for fake news detection.
- 用户更可能传播他感兴趣的虚假新闻。这篇文章研究一个新的问题，在虚假新闻检测中利用用户偏好信息。
- CIKM-2020 [FANG : Leveraging Social Context for Fake News Detection Using Graph Representation](https://dl.acm.org/doi/abs/10.1145/3340531.3412046?casa_token=33FpLHu6h20AAAAA:fc2L3COGdQCca7fS2l4rOjcP_LzmDMVI1fROs9Yxi0m7xTuyQUpec9sm6MZe0_Yli7Vo4tcDh6nURN8) [code](https://github.com/nguyenvanhoang7398/FANG).
- This paper proposed Factual News Graph (FANG) for fake news detection, which is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph.
- 这篇文章提出了FANG来解决虚假新闻检测问题，FANG在训练中具有可扩展性，不必维护所有节点，并且在推理时非常高效，而无需重新处理整个图。
- ICDM-2020 [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://ieeexplore.ieee.org/abstract/document/9338358/).
- This paper attempted to solve the fake news detection problem with the support of a news-oriented HIN and proposed a novel method AA-HGNN. AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
- 这篇文章尝试基于新闻导向的异构信息网络解决虚假新闻检测的方法，并且提出了一个新的方法AA-HGNN。AA-HGNN使用主动学习的框架来增强学习表现，特别是面对标签数据不足的情况。
- WSDM-2019 [Beyond News Contents : The Role of Social Context for Fake News Detection](https://dl.acm.org/doi/abs/10.1145/3289600.3290994?casa_token=vzRcFcZbogkAAAAA:CgTc3CqhxgZ3JqqwPLrCAz_vVP2wShHGZvZnLZdeM2Evss5Uqu4-L1UUhLVB-G62_hfT-WcqZLW52gY)
- This paper proposed a tri-relationship embedding framework (tensor factorization method) TriFN, which models publisher-news relations and user-news interactions simultaneExcerpt of 23,738 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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