mlsanityData to 7 Oct 2026

A Survey on Natural Language Processing for Fake News Detection

arXiv 1811.00770 ↗First posted 2 Nov 2018cs.CL cs.AIcited by 67

Not on the map. Why?

Abstract (arXiv)

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 12; 2023: 16; 2024: 14; 2025: 7; 2026: 3.

67 papers.

Sort:

By area:

  1. 2021A Survey on Automated Fact-Checkingcited by 376
  2. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  3. 2022Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Surveycited by 80
  4. 2023A Survey on Detection of LLMs-Generated Contentcited by 70
  5. 2023Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformationcited by 51
  6. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  7. 2023Zero-Shot Detection of Machine-Generated Codescited by 24
  8. 2021Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake Newscited by 23
  9. 2022Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabiccited by 18
  10. 2023Cheap-fake Detection with LLM using Prompt Engineeringcited by 15
  11. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  12. 2023Adapting Fake News Detection to the Era of Large Language Modelscited by 14
38 more
  1. 2024Self-Evaluation as a Defense Against Adversarial Attacks on LLMscited by 13
  2. 2022Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-Tuningcited by 12
  3. 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
  4. 2024Blessing or curse? A survey on the Impact of Generative AI on Fake Newscited by 11
  5. 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
  6. 2023On the Uses of Large Language Models to Interpret Ambiguous Cyberattack Descriptionscited by 10
  7. 2024A Guide to Misinformation Detection Data and Evaluationcited by 10
  8. 2021VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Textcited by 9
  9. 2024Native vs Non-Native Language Prompting: A Comparative Analysiscited by 9
  10. 2024Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checkingcited by 8
  11. 2024Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detectioncited by 7
  12. 2021NewsEmbed: Modeling News through Pre-trained Document Representationscited by 6
  13. 2021Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Languagecited by 6
  14. 2023Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciencescited by 6
  15. 2023CT-GAT: Cross-Task Generative Adversarial Attack based on Transferabilitycited by 6
  16. 2021Identifying Misinformation from Website Screenshotscited by 5
  17. 2022Automatic Detection of Entity-Manipulated Text using Factual Knowledgecited by 5
  18. 2022Measuring Geographic Performance Disparities of Offensive Language Classifierscited by 5
  19. 2023Discourse Structures Guided Fine-grained Propaganda Identificationcited by 5
  20. 2021Challenges and Considerations with Code-Mixed NLP for Multilingual Societiescited by 4
  21. 2022Multilingual Persuasion Detection: Video Games as an Invaluable Data Source for NLPcited by 4
  22. 2022MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detectioncited by 4
  23. 2024Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approachcited by 4
  24. 2024Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkishcited by 4
  25. 2024Fine-tuned Large Language Models (LLMs): Improved Prompt Injection Attacks Detectioncited by 4
  26. 2025From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPTcited by 4
  27. 2021A Survey on Awesome Korean NLP Datasetscited by 3
  28. 2022Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detectioncited by 3
  29. 2023HonestBait: Forward References for Attractive but Faithful Headline Generationcited by 3
  30. 2024Detecting misinformation through Framing Theory: the Frame Element-based Modelcited by 3
  31. 2025Context Engineering for Trustworthiness: Rescorla Wagner Steering Under Mixed and Inappropriate Contextscited by 3
  32. 2021Symbiotic System of Systems Design for Safe and Resilient Autonomous Robotics in Offshore Wind Farmscited by 2
  33. 2021Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemiccited by 2
  34. 2025Deconfounded Reasoning for Multimodal Fake News Detection via Causal Interventioncited by 2
  35. 2025Jensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learningcited by 2
  36. 2023Detecting Contextomized Quotes in News Headlines by Contrastive Learningcited by 1
  37. 2023Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detectioncited by 1
  38. 2023Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Timecited by 1

50 most cited of 67 All 67 (JSON)