Journal ArticleDOI
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media
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TLDR
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.Abstract:
Social media has become a popular means for people to consume and share the news. At the same time, however, it has also enabled the wide dissemination of fake news, that is, news with intentionally false information, causing significant negative effects on society. To mitigate this problem, the research of fake news detection has recently received a lot of attention. Despite several existing computational solutions on the detection of fake news, the lack of comprehensive and community-driven fake news data sets has become one of major roadblocks. Not only existing data sets are scarce, they do not contain a myriad of features often required in the study such as news content, social context, and spatiotemporal information. Therefore, in this article, to facilitate fake news-related research, we present a fake news data repository FakeNewsNet, which contains two comprehensive data sets with diverse features in news content, social context, and spatiotemporal information. We present a comprehensive description of the FakeNewsNet, demonstrate an exploratory analysis of two data sets from different perspectives, and discuss the benefits of the FakeNewsNet for potential applications on fake news study on social media.read more
Citations
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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.
Journal ArticleDOI
A survey on fake news and rumour detection techniques
TL;DR: This paper surveys the different approaches to automatic detection of fake news and rumours proposed in the recent literature and provides a comprehensive analysis on the various techniques used to perform rumour and 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.
Journal ArticleDOI
Combating Fake News: A Survey on Identification and Mitigation Techniques
TL;DR: This survey describes the modern-day problem of fake news and, in particular, highlights the technical challenges associated with it and comprehensively compile and summarize characteristic features of available datasets.
Journal ArticleDOI
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Xinyi Zhou,Reza Zafarani +1 more
TL;DR: In this paper, a survey of methods that can detect fake news from four perspectives: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its source.
References
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Posted Content
Echo Chambers: Emotional Contagion and Group Polarization on Facebook
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Book ChapterDOI
Studying Fake News via Network Analysis: Detection and Mitigation
TL;DR: This chapter will review network properties for studying fake news, introduce popular network types, and propose how these networks can be used to detect and mitigate fake news on social media.
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