K
Kai Shu
Researcher at Illinois Institute of Technology
Publications - 152
Citations - 7774
Kai Shu is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 25, co-authored 95 publications receiving 4656 citations. Previous affiliations of Kai Shu include Arizona State University & Chinese Academy of Sciences.
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Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.
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Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
Journal ArticleDOI
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
TL;DR: A fake news data repository FakeNewsNet is presented, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information, and is discussed for potential applications on fake news study on social media.
Proceedings ArticleDOI
dEFEND: Explainable Fake News Detection
TL;DR: A sentence-comment co-attention sub-network is developed to exploit both news contents and user comments to jointly capture explainable top-k check-worthy sentences and userComments for fake news detection.
Proceedings ArticleDOI
Beyond News Contents: The Role of Social Context for Fake News Detection
Kai Shu,Suhang Wang,Huan Liu +2 more
TL;DR: Li et al. as discussed by the authors proposed a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification and showed that the proposed approach significantly outperforms other baseline methods.