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fake-news-detection
This repo is a collection of AWESOME things about fake news detection, including papers, code, etc.
https://github.com/ICTMCG/fake-news-detection
Last synced: about 19 hours ago
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<span id="scholars">Distinguished Scholars in Fake News Detection</span> (虚假新闻检测领域杰出学者)
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<span id="transfer">Transfer Learning</span> 迁移学习
- Reza Zafarani
- Juan Cao
- Preslav Nakov
- Kyumin Lee
- Kai Shu - 6bAV2cAAAAJ)
- Jing Ma
- Reza Zafarani
- Huan Liu
- Juan Cao
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<span id="paper">Papers</span>
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<span id="social">Social Context</span> 社交上下文
- News Verification by Exploiting Conflicting Social Viewpoints in Microblogs
- Causal Understanding of Fake News Dissemination on Social Media
- User Preference-aware Fake News Detection - graph/GNN-FakeNews)
- Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
- Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks
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<span id="content-base">News Contents</span> 新闻内容
- Multimodal Emergent Fake News Detection via Meta Neural Process Networks
- Mining Dual Emotion for Fake News Detection
- Exploiting Multi-domain Visual Information for Fake News Detection
- A stylometric inquiry into hyperpartisan and fake news
- Learning hierarchical discourse-level structure for fake news detection
- Multimodal Emergent Fake News Detection via Meta Neural Process Networks
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<span id="fact">Fact Checking</span> 真实性检验
- Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
- Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
- Fine-grained Fact Verification with Kernel Graph Attention Network
- DeClarE : Debunking Fake News and False Claims using Evidence-Aware Deep Learning
- The rise of guardians: Fact-checking URL recommendation to combat fake news
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<span id="transfer">Transfer Learning</span> 迁移学习
- DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation
- Generalizing to the Future: Mitigating Entity Bias in Fake News Detection - SIGIR2022)
- Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
- Different Absorption from the Same Sharing : Sifted Multi-task Learning for Fake News Detection
- Detect Rumor and Stance Jointly by Neural Multi-task Learning
- A topic-agnostic approach for identifying fake news pages
- DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation
- MDFEND: Multi-domain Fake News Detection - Weibo21)
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<span id="survey">Survey</span> 综述
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<span id="explainable">Explainable</span> 可解释
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<span id="datasets">Datasets</span>
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<span id="transfer">Transfer Learning</span> 迁移学习
- LIAR (ACL2017) - domain Fake News Detection using Multi-modal Data](https://www.aclweb.org/anthology/P17-2067.pdf)
- FakeHealth (AAAI 2021)
- FakeHealth (AAAI 2021)
- FacebookHoax
- BuzzFeedNews - fb-pages-analysis)
- CoAID - 19 Healthcare Misinformation Dataset](https://arxiv.org/pdf/2006.00885.pdf)
- FakeNewsNet (Journal of Big Data 2020)
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