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
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Book ChapterDOI
Identification of Salient Attributes in Social Network: A Data Mining Approach
TL;DR: This study is based on a dataset originally drawn from the Facebook social network page of a large multinational cosmetics company, and shows that not all predictors are significant in explaining the criterion variable.
Journal ArticleDOI
FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms
TL;DR: Wang et al. as discussed by the authors constructed the largest Chinese short video dataset about fake news named FakeSV, which includes news content, user comments, and publisher profiles simultaneously, and provided a new multimodal detection model named SV-FEND, which exploits the cross-modal correlations to select the most informative features and utilizes the social context information for detection.
Proceedings ArticleDOI
Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation
Jeff Da,Maxwell Forbes,Rowan Zellers,Anthony Zheng,Jena D. Hwang,Antoine Bosselut,Yejin Choi +6 more
TL;DR: This paper proposed a new formalism called Edited Media Frames (EMU) to understand visual media manipulation as structured annotations with respect to the intents, emotional reactions, attacks on individuals, and the overall implications of disinformation.
Journal ArticleDOI
Language does not modulate fake news credibility, but emotion does
TL;DR: In this article, the authors conducted two experiments to examine whether fake news stories presented to university students were more credible in the native language than in a foreign language and found a strong relationship between credibility and negative emotionality regardless of language.
Proceedings ArticleDOI
Theorizing hybrid models of peer production: a case study of an open collaborative journalism platform
TL;DR: A theoretical framework is presented to analyze case study findings from the WikiTribune project, a “hybrid” model of peer production that combines elements of commercial firm-based production with that of commons-based peer production.
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