A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Abstract
The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style , its propagation patterns, and the credibility of its source . The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.
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Fake news poses a substantial threat to democracy, journalism, and the fundamental principles of freedom of expression.
“This prevalent phenomenon, commonly referred to as”
From Deepfakes: current and future trends (2024) · cites this paper as Zhou and Zafarani 2020
Identifying fake text falls within a well-established domain, with many state-of-the-art algorithms and techniques available to tag fake text with significant accuracy.
“The latter process, which is fake text identification, falls within a well-established domain, with many state-of-the-art algorithms and techniques available to tag fake text with significant accuracy Zhou & Zafarani (2020).”
From Decoding ChatGPT: A taxonomy of existing research, current challenges, and possible future directions (2023) · cites this paper as Zhou & Zafarani (2020)
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- Authors
- Zhou, X., & Zafarani, R.
- Affiliations
- Syracuse University (US)
- Venue
- ACM Computing Surveys
- Published
- 2020-09-28
- Volume
- 53
- Issue
- 5
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4288079542
- Cited by
- 1,184
- References
- 18
- Citations by year
- 2026: 228 · 2025: 272 · 2024: 183 · 2023: 192 · 2022: 176 · 2021: 123 · 2020: 9 · 2013: 1
- FWCI
- 167.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (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, misinformation detection, false knowledge, writing style, propagation patterns, source credibility, democracy, public trust, explainable detection, interdisciplinary research
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