Fake News: Fundamental Theories, Detection Strategies and Challenges
Abstract
The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumors); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.
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Research from computer science and social network analysis has provided insights on navigating polluted data streams, especially concerning effects of fraudulent reviews, fake social network accounts identification, and fake news detection.
“Research from the computer science and social network analysis fields have recently provided valuable insights on how to navigate through polluted data streams, especially concerning the effects produced by fraudulent reviews (Ananthakrishnan et al., 2015), fake social network accounts identification (Sarna & Bhatia, 2018) and fake news detection (Zhou, Zafarani, Shu, & Liu, 2019).”
From Setting the future of digital and social media marketing research: Perspectives and research propositions (2021) · cites this paper as Zhou, Zafarani, Shu, & Liu, 2019
Cites
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767 · see citing works
Jindal, N., & Liu, B. (2008) · Proceedings of the international conference on Web search and web data mining - WSDM '08 · doi:10.1145/1341531.1341560 · Cited by 1,542 · see citing works
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Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102168 · Cited by 2,620 · see citing works
“…valuable insights on how to navigate through polluted data streams, especially concerning the effects produced by fraudulent reviews (Ananthakrishnan et al., 2015), fake social network accounts identification (Sarna & Bhatia, 2018) and fake news detection (Zhou, Zafarani, Shu, & Liu, 2019).”
BackgroundPennycook, G., & Rand, D. G. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.02.007 · Cited by 1,360 · see citing works
Multimodal fake news detection via progressive fusion networks
Jing, J., Wu, H., Sun, J., Fang, X., & Zhang, H. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2022.103120 · Cited by 243 · see citing works
Toward Mitigating Misinformation and Social Media Manipulation in LLM Era
Zhang, Y., Sharma, K., Du, L., & Liu, Y. (2024) · Companion Proceedings of the ACM Web Conference 2024 · doi:10.1145/3589335.3641256 · Cited by 25 · see citing works
- Authors
- Zhou, X., Zafarani, R., Shu, K., & Liu, H.
- Affiliations
- Syracuse University (US)
- Arizona State University (US)
- Venue
- Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2019-01-30
- Pages
- 836-837
- OpenAlex
- W2912642460
- Cited by
- 256
- References
- 2
- Citations by year
- 2026: 7 · 2025: 29 · 2024: 32 · 2023: 32 · 2022: 55 · 2021: 49 · 2020: 33 · 2019: 15 · 2018: 4
- FWCI
- 108.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- fake news detection, fake news, misinformation, rumors, social networks, news content analysis, information retrieval, social search, 2020 US presidential election, public trust, democracy
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