Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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
Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate
TL;DR: A novel approach to modeling the propagation of messages in a social network, TraceMiner, to infer embeddings of social media users with social network structures and utilize an LSTM-RNN to represent and classify propagation pathways of a message.
Proceedings ArticleDOI
On the Detection of Digital Face Manipulation
TL;DR: Zhang et al. as mentioned in this paper proposed to utilize an attention mechanism to process and improve the feature maps for the classification task and showed that the learned attention maps highlight the informative regions to further improve the binary classification and visualize the manipulated regions.
Journal ArticleDOI
Why do People Share Misinformation during the COVID-19 Pandemic?
TL;DR: In this paper, the authors developed and tested a research model hypothesizing why people share unverified COVID-19 information through social media and found that a person's trust in online information and perceived information overload are strong predictors of unverified information sharing.
Proceedings ArticleDOI
Understanding User Profiles on Social Media for Fake News Detection
Kai Shu,Suhang Wang,Huan Liu +2 more
TL;DR: A comparative analysis over explicit and implicit profile features between “experienced” users who are able to recognize fake news items as false and “naïve” Users who are more likely to believe fake news reveals their potential to differentiate fake news.
Journal ArticleDOI
The Psychology of Fake News
Gordon Pennycook,David G. Rand +1 more
TL;DR: The authors synthesize a burgeoning literature investigating why people believe and share false or highly misleading news online and find that people are better at discerning truth from falsehood when evaluating politically concordant news.
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