# Detecting Fake News Using Machine Learning : A Systematic Literature Review

arXiv: https://arxiv.org/abs/2102.04458 · HTML: https://mlsanity.com/paper/2102.04458/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 5 of 5)

- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [A Benchmark Study of Machine Learning Models for Online Fake News Detection](https://mlsanity.com/paper/1905.04749/) (2019)
- [Improving Generalizability of Fake News Detection Methods using Propensity Score Matching](https://mlsanity.com/paper/2002.00838/) (2020)

## Built on by (most cited first, 19 of 19)

- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [Do Language Models Plagiarize?](https://mlsanity.com/paper/2203.07618/) (2022)
- [AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees](https://mlsanity.com/paper/2510.01268/) (2025)
- [DALD: Improving Logits-based Detector without Logits from Black-box LLMs](https://mlsanity.com/paper/2406.05232/) (2024)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm](https://mlsanity.com/paper/2509.15550/) (2025)
- [Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool](https://mlsanity.com/paper/2402.19135/) (2024)
- [Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction](https://mlsanity.com/paper/2505.05084/) (2025)
- [Large Language Models for Propaganda Detection](https://mlsanity.com/paper/2310.06422/) (2023)
- [Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection](https://mlsanity.com/paper/2510.08602/) (2025)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [TempTest: Local Normalization Distortion and the Detection of Machine-generated Text](https://mlsanity.com/paper/2503.20421/) (2025)
- [Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2501.18265/) (2025)
- [Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework](https://mlsanity.com/paper/2505.12507/) (2025)
- [NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs](https://mlsanity.com/paper/2506.11050/) (2025)
- [Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration](https://mlsanity.com/paper/2608.05741/) (2026)
- [Biased by Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers](https://mlsanity.com/paper/2504.14522/) (2025)
- [Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection](https://mlsanity.com/paper/2603.24981/) (2026)
- [Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks](https://mlsanity.com/paper/2606.31074/) (2026)

## 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.