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
A transformer-based architecture for fake news classification
TL;DR: In this article, the authors focus on classifying fake news using models based on a natural language processing framework, Bidirectional Encoder Representations from Transformers, also known as BERT.
Journal ArticleDOI
Testing Children and Adolescents’ Ability to Identify Fake News: A Combined Design of Quasi-Experiment and Group Discussions
TL;DR: In this paper, the authors explored the vulnerability of students to fake news and the way they experience an experimental situation in which they are exposed to online fake information, and found that participants proved that they would act upon being exposed to fake information even when they do not trust the source.
Journal ArticleDOI
Arresting fake news sharing on social media: a theory of planned behavior approach
TL;DR: In this paper, the collective impact of awareness and knowledge about fake news, attitudes toward news verification, perceived behavioral control, subjective norms, fear of missing out (FoMO), and sadism on social media users' intention to verify news before sharing on Twitter was examined.
Journal ArticleDOI
A systematic mapping on automatic classification of fake news in social media
João Victor de Souza,Jorão Gomes,Fernando Marques de Souza Filho,Alessandreia Marta de Oliveira Julio,Jairo Francisco de Souza +4 more
TL;DR: This work covers eight years of research on fake news applied in social media and presents the main methods, text and user features, and datasets used in literature.
Proceedings ArticleDOI
Fake news detection using deep Markov random fields
TL;DR: Zhang et al. as discussed by the authors formulated fake news detection as an inference problem in a Markov random field (MRF) which can be solved by the iterative mean-field algorithm.
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