mlsanityData to 7 Oct 2026

Detecting Fake News Using Machine Learning : A Systematic Literature Review

arXiv 2102.04458 ↗First posted 8 Feb 2021cs.CY cs.LGcited by 19

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Abstract (arXiv)

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through the online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. These news can be propaganda against an individual, society, organization or political party. A human being is unable to detect all these fake news. So there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting fake news is described in this systematic literature review.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 5 of its 37 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

5 of 37 references.

References · 5

  1. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  2. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  3. 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 103
  4. 2019A Benchmark Study of Machine Learning Models for Online Fake News Detectioncited by 6
  5. 2020Improving Generalizability of Fake News Detection Methods using Propensity Score Matchingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 9; 2026: 3.

19 papers.

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  1. 2023Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvaturecited by 278
  2. 2022Do Language Models Plagiarize?cited by 72
  3. 2025AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guaranteescited by 20
  4. 2024DALD: Improving Logits-based Detector without Logits from Black-box LLMscited by 12
  5. 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
  6. 2025Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Predictioncited by 10
  7. 2025DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigmcited by 10
  8. 2024Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Toolcited by 9
  9. 2025Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detectioncited by 8
  10. 2022A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacousticcited by 7
  11. 2023Large Language Models for Propaganda Detectioncited by 7
  12. 2025TempTest: Local Normalization Distortion and the Detection of Machine-generated Textcited by 3
7 more
  1. 2025Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checkingcited by 2
  2. 2025Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Frameworkcited by 1
  3. 2026Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restorationcited by 1
  4. 2025Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readerscited by 0
  5. 2025NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMscited by 0
  6. 2026Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detectioncited by 0
  7. 2026Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attackscited by 0