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Detecting Fake News Using Machine Learning : A Systematic Literature Review

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arxiv 2102.04458 v1 pith:QIB4HA7C submitted 2021-02-08 cs.CY cs.LG

Alim Al Ayub Ahmed , Ayman Aljabouh , Praveen Kumar Donepudi , Myung Suh Choi This is my paper · ORCID
classification cs.CYcs.LG
keywords newsfakeplatformsuserslearningmachineclassifiersdetect
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMs

    cs.LG 2025-05 reject novelty 6.0 of 10

    LLMs forecast electricity prices worse than ARIMA on the new NSW-EPNews benchmark and frequently hallucinate by echoing, offsetting, or repeating historical prices.

  2. Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration

    cs.CL 2026-08 conditional novelty 4.0 of 10

    EchoPrompt detects LLM-generated text by measuring the likelihood gain from restoring a generic assistant-style prompt, calibrated against a base model.

  3. Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework

    cs.CL 2025-05 reject novelty 4.0 of 10

    LM2OTIFS uses word co-occurrence graphs and GNNExplainer to detect and explain machine-generated text, with strong in-domain accuracy but unsupported faithfulness claims and a flawed theoretical proof.

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