Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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TLDR
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.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present 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. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
Citations
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Journal ArticleDOI
The spread of low-credibility content by social bots.
Chengcheng Shao,Giovanni Luca Ciampaglia,Onur Varol,Kai-Cheng Yang,Alessandro Flammini,Filippo Menczer +5 more
TL;DR: It is found that bots play a major role in the spread of low-credibility content on Twitter, and control measures for limiting thespread of misinformation are suggested.
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.
Posted Content
Fake News Detection on Social Media using Geometric Deep Learning
TL;DR: A novel automatic fake news detection model based on geometric deep learning that can be reliably detected at an early stage, after just a few hours of propagation, and the results point to the promise of propagation-based approaches forfake news detection as an alternative or complementary strategy to content-based approach.
Journal ArticleDOI
Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior
Shalini Talwar,Amandeep Dhir,Amandeep Dhir,Puneet Kaur,Puneet Kaur,Nida Zafar,Melfi Alrasheedy +6 more
TL;DR: In this paper, the authors examined the associations of the dark side of social media use and fake news sharing behavior among social media users and found that online trust, self-disclosure, fear of missing out, and social media fatigue are positively associated with the sharing fake news.
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