mlsanityData to 14 Oct 2026

A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities

arXiv 1812.00315 ↗First posted 2 Dec 2018cs.CL cs.AI cs.SIcited by 28

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Abstract (arXiv)

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: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) 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. We hope 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.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Dec 2018.

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Citing papers per year: 2021: 11; 2022: 8; 2023: 4; 2024: 3; 2025: 2.

28 papers.

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  1. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 118
  2. 2021The Surprising Performance of Simple Baselines for Misinformation Detectioncited by 28
  3. 2021Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detectioncited by 21
  4. 2021FakeFlow: Fake News Detection by Modeling the Flow of Affective Informationcited by 20
  5. 2021Misinformation Detection on YouTube Using Video Captionscited by 12
  6. 2021A Survey on Echo Chambers on Social Media: Description, Detection and Mitigationcited by 12
  7. 2021How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?cited by 7
  8. 2024A Multi-Label Dataset of French Fake News: Human and Machine Insightscited by 5
  9. 2021TI-Capsule: Capsule Network for Stock Exchange Predictioncited by 4
  10. 2022Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2021cited by 4
  11. 2021NoFake at CheckThat! 2021: Fake News Detection Using BERTcited by 3
  12. 2023Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signalscited by 3
16 more
  1. 2021A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irmacited by 2
  2. 2021FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Featurescited by 1
  3. 2022Are You Misinformed? A Study of Covid-Related Fake News in Bengali on Facebookcited by 1
  4. 2022Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approachcited by 1
  5. 2023Detecting Fake Job Postings Using Bidirectional LSTMcited by 1
  6. 2023Written and spoken corpus of real and fake social media postings about COVID-19cited by 1
  7. 2025ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interactioncited by 1
  8. 2021ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSscited by 0
  9. 2022A Unified Training Process for Fake News Detection based on Fine-Tuned BERT Modelcited by 0
  10. 2022Learning Robust Representations Of Generative Models Using Set-Based Artificial Fingerprintscited by 0
  11. 2022Multilingual Disinformation Detection for Digital Advertisingcited by 0
  12. 2022Deep Fake Detection, Deterrence and Response: Challenges and Opportunitiescited by 0
  13. 2023No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentationcited by 0
  14. 2024Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformationcited by 0
  15. 2024Analysis of child development facts and myths using text mining techniques and classification modelscited by 0
  16. 2025Enhancing Social Media Rumor Detection: A Semantic and Graph Neural Network Approach for the 2024 Global Electioncited by 0