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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Fake News Detection using Stance Classification: A Survey.
TL;DR: This paper surveys and presents recent academic work carried out within the field of stance classification and fake news detection and includes a proposal of a system implementation based on the presented survey.
Journal ArticleDOI
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User trustworthiness in online social networks: A systematic review
TL;DR: In this article, the authors present an analysis of the studies published between 2012 and 2020 that attempt to address the problem using various methodologies, such as bot identification protocols, anti-spam protection, recognition of fake news, or rating the truthfulness of user-generated content.
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Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms
TL;DR: This article conducted an in-depth analysis of the 2018 US mid-term elections looking specifically for voter fraud or suppression, and identified several biases that can lead to wrong conclusions, such as population bias, or bot and political ideology inference.
Journal ArticleDOI
Fake news detection based on explicit and implicit signals of a hybrid crowd: An approach inspired in meta-learning
TL;DR: HCS, an approach based on crowd signals that considers implicit user opinions instead of the explicit ones that is inspired in Meta-Learning, is proposed, able to achieve results comparable to the ones produced by the Crowd Signals approach.
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