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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Journal ArticleDOI
Improvement of K-Means Algorithm for Accelerated Big Data Clustering
Chunqiong Wu,Bingwen Yan,Rongrui Yu,Zhangshu Huang,Baoqin Yu,Yanliang Yu,Na Chen,Xiukao Zhou +7 more
TL;DR: In view of some defects exposed by the traditional k-means algorithm, this paper mainly improves and analyzes from two aspects.
Proceedings ArticleDOI
The risks of new technologies in Black Mirror: A content analysis of the depiction of our current socio-technological reality in a TV series
TL;DR: It is observed that social media, smartphones and tablets and technological implants in humans are the most present technologies in the Black Mirror series; technology is depicted under a rather negative perspective in the series, with generally negative effects over societies, and full of risks.
Proceedings ArticleDOI
Do you have a source for that?: understanding the challenges of collaborative evidence-based journalism
TL;DR: The rich literature on Wikipedia is used to understand the WikiTribune case and to identify areas of convergence and divergence, as well as avenues for future research.
Proceedings ArticleDOI
Designing Media Provenance Indicators to Combat Fake Media
TL;DR: In this paper, the authors conduct a mixed-methods investigation into how to provide provenance indicators to assist users in detecting newer forms of fake media, such as edited images and manipulated videos.
Journal ArticleDOI
A smart contract logic to reduce hoax propagation across social media
TL;DR: In this paper , the authors proposed a mechanism based on smart contract logics to prevent a group to consume a fake post by using a trust index computed based on message characteristics and group features such as graph density, group status, group degree, group acceptability.
References
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Amos Tversky,Daniel Kahneman +1 more
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