Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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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Proceedings ArticleDOI
Detection of Satiric News on Social Media: Analysis of the Phenomenon with a French Dataset
TL;DR: This paper contributes a useful French satiric dataset to the research community and provides a satiric news detection system using machine learning to automate classifications significantly, and presents the preliminary results of the research designed to discriminate real news from satiric stories, and thus ultimately reduce false and satiric News distribution.
Proceedings ArticleDOI
Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation
TL;DR: A novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news, which significantly outperforms the state-of-the-art news recommendation methods.
Journal ArticleDOI
Combating Fake News in “Low-Resource” Languages: Amharic Fake News Detection Accompanied by Resource Crafting
TL;DR: In this article, a fake news detection model for low-resource African languages, such as Amharic, is presented, evaluated with the ETH_FAKE dataset and using the AMFTWE, performed very well.
Journal ArticleDOI
When classification accuracy is not enough: Explaining news credibility assessment
TL;DR: The adapted neural classifier showed better performance on the test data than the stylometric classifier, despite the latter appearing to be easier to interpret by the participants, and users were significantly more accurate in their assessment after they interacted with the tool as well as more confident with their decisions.
Proceedings ArticleDOI
Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media
TL;DR: A fake news detection model named Post-User Interaction Network (PSIN) is proposed, which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics.
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