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Fake News Detection on Social Media: A Data Mining Perspective

Authors:

Huan LiuAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 19, Issue 1

Pages 22 - 36

https://doi.org/10.1145/3137597.3137600

Published: 01 September 2017 Publication History

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.

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Roumeliotis KTselikas NNasiopoulos D(2025)Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing ModelsFuture Internet10.3390/fi1701002817:1(28)Online publication date: 9-Jan-2025
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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 19, Issue 1

June 2017

59 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3137597

Editors:
Charu Aggarwal
IBM T.J. Watson
,
Haixun Wang
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Hanghang Tong
Arizona State University

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Copyright © 2017 Authors.

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Published: 01 September 2017

Published in SIGKDD Volume 19, Issue 1

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Roumeliotis KTselikas NNasiopoulos D(2025)Fake News Detection and Classification: A Comparative Study of Convolutional Neural Networks, Large Language Models, and Natural Language Processing ModelsFuture Internet10.3390/fi1701002817:1(28)Online publication date: 9-Jan-2025
https://doi.org/10.3390/fi17010028
Jin WGao YTao TWang XWang NWu BZhao B(2025)Veracity‐Oriented Context‐Aware Large Language Models–Based Prompting Optimization for Fake News DetectionInternational Journal of Intelligent Systems10.1155/int/59201422025:1Online publication date: 15-Jan-2025
https://doi.org/10.1155/int/5920142
Ma XLiu FWu JYang JXue SSheng Q(2025)Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and ObjectivesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350130737:2(881-895)Online publication date: Feb-2025
https://doi.org/10.1109/TKDE.2024.3501307
Kumar RGoddu BSaha SJatowt A(2025)Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?IEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34402486:1(14-24)Online publication date: Jan-2025
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