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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Proceedings ArticleDOI
News labeling as early as possible: real or fake?
TL;DR: In this article, a new stopping rule was proposed to identify fake and real news in early stages of propagation by using a recurrent neural network with a novel loss function and a stopping rule to minimize the time gap between news release time and detection of its label.
Proceedings Article
A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN
TL;DR: In this paper, a bi-directional recurrent neural network (RNN) classification model was trained on interpretable features derived from multi-disciplinary integrated approaches to language and applied to two benchmark datasets.
Proceedings ArticleDOI
To Intervene or Not To Intervene: Cost based Intervention for Combating Fake News
TL;DR: In this paper, a cost-aware intervention policy which decides whether to intervene based on the truthiness and popularity of the item is proposed, which consists of three modular components -truthiness estimation, popularity estimation (with and without intervention), and intervention policy.
Book ChapterDOI
An Approach to Creating an Intelligent System for Detecting and Countering Inappropriate Information on the Internet
TL;DR: A new approach to creating an intelligent system for detecting and counteracting inappropriate information on the Internet based on the use of machine learning methods and processing of big data is offered and the architecture of such a system is described.
Journal ArticleDOI
Deception detection on social media: A source-based perspective
TL;DR: In this paper , a source-based method in a machine learning framework was proposed to detect fake news, rumors, conspiracies, hoaxes, and other forms of deception in online social networks.
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