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
Science Factionalism: How Group Identity Language Affects Public Engagement With Misinformation and Debunking Narratives on a Popular Q&A Platform in China
TL;DR: This paper examined how identity language is used in misinformation and debunking messages about controversial science on the Chinese digital public spheres and their impact on how the public engage with science and found that posts that use science factionalism receive more digital votes and comments, even among the science-savvy community.
Journal ArticleDOI
Modelling election dynamics and the impact of disinformation
TL;DR: Dynamical dynamics of electoral competition is modelled by the specification of the flow of information relevant to election, which is used to study election prediction, impact of disinformation, and the optimal strategy for information management in an election campaign.
Proceedings ArticleDOI
Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News Sites
TL;DR: In this article , the authors identify the companies that advertise in fake news websites and the intermediary companies responsible for facilitating those ad revenues, and explore who supports the existence of fake news sites via paid ads, either as an advertiser or an ad seller.
Proceedings ArticleDOI
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts
Mohit Chandra,Ashwin Pathak,Eesha Dutta,Paryul Jain,Manish Gupta,Manish Shrivastava,Ponnurangam Kumaraguru +6 more
TL;DR: In this paper, the authors present a dataset with 7,601 posts from Gab which looks at online abuse from the perspective of presence of abuse, severity and target of abusive behavior.
Proceedings ArticleDOI
Exploring the Impact of Machine Translation on Fake News Detection: A Case Study on Persian Tweets about COVID-19
TL;DR: In this paper, the authors explored the impacts of machine translation on fake news detection in low resource languages like Persian and found that machine translation has a 4 % negative impact on binary classification accuracy and a 23% negative effect on multiclass classification.
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