mlsanityData to 7 Oct 2026

Fine-Grained Analysis of Propaganda in News Articles

arXiv 1910.02517 ↗First posted 6 Oct 2019cs.CL cs.AI cs.IRcited by 130

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Abstract (arXiv)

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.

Key facts. Cited by 130 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 25; 2022: 17; 2023: 28; 2024: 19; 2025: 19; 2026: 22.

130 papers.

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  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2022Detecting and Understanding Harmful Memes: A Surveycited by 58
  3. 2021SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Imagescited by 49
  4. 2021A Survey on Multimodal Disinformation Detectioncited by 47
  5. 2022Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformationcited by 45
  6. 2021Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classificationcited by 43
  7. 2021Detecting Propaganda Techniques in Memescited by 43
  8. 2022Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generationcited by 41
  9. 2021Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlinescited by 32
  10. 2024Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Languagecited by 31
  11. 2021MUDES: Multilingual Detection of Offensive Spanscited by 30
  12. 2023Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applicationscited by 27
38 more
  1. 2022Logical Fallacy Detectioncited by 25
  2. 2023Multitask Instruction-based Prompting for Fallacy Recognitioncited by 22
  3. 2022ProtoTEx: Explaining Model Decisions with Prototype Tensorscited by 21
  4. 2024Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articlescited by 21
  5. 2023ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Textcited by 19
  6. 2023Large Language Models for Propaganda Span Annotationcited by 19
  7. 2024Literature Meets Data: A Synergistic Approach to Hypothesis Generationcited by 19
  8. 2021RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversationscited by 18
  9. 2022Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabiccited by 18
  10. 2021Interpretable Propaganda Detection in News Articlescited by 16
  11. 2022KOLD: Korean Offensive Language Datasetcited by 14
  12. 2024Missci: Reconstructing Fallacies in Misrepresented Sciencecited by 14
  13. 2023Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?cited by 13
  14. 2021Transformers: "The End of History" for NLP?cited by 12
  15. 2022Robust and Explainable Identification of Logical Fallacies in Natural Language Argumentscited by 12
  16. 2024ArAIEval Shared Task: Propagandistic Techniques Detection in Unimodal and Multimodal Arabic Contentcited by 11
  17. 2025PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformationcited by 11
  18. 2021ToxCCIn: Toxic Content Classification with Interpretabilitycited by 10
  19. 2021ENTRUST: Argument Reframing with Language Models and Entailmentcited by 10
  20. 2021On Unifying Misinformation Detectioncited by 10
  21. 2021The Spread of Propaganda by Coordinated Communities on Social Mediacited by 10
  22. 2024Are LLMs Good Zero-Shot Fallacy Classifiers?cited by 10
  23. 2023Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classificationcited by 9
  24. 2024Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Toolcited by 9
  25. 2024PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intentcited by 9
  26. 2023Case-Based Reasoning with Language Models for Classification of Logical Fallaciescited by 8
  27. 2023Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Mediacited by 8
  28. 2023Identifying Conspiracy Theories News based on Event Relation Graphcited by 8
  29. 2024Propaganda to Hate: A Multimodal Analysis of Arabic Memes with Multi-Agent LLMscited by 8
  30. 2024CoCoLoFa: A Dataset of News Comments with Common Logical Fallacies Written by LLM-Assisted Crowdscited by 8
  31. 2025MemeIntel: Explainable Detection of Propagandistic and Hateful Memescited by 8
  32. 2025Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detectioncited by 8
  33. 2021Mitigating Media Bias through Neutral Article Generationcited by 7
  34. 2022PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Taskscited by 7
  35. 2023Large Language Models for Propaganda Detectioncited by 7
  36. 2025PropXplain: Can LLMs Enable Explainable Propaganda Detection?cited by 7
  37. 2025LLMs as Span Annotators: A Comparative Study of LLMs and Humanscited by 7
  38. 2025LLM-based Semantic Augmentation for Harmful Content Detectioncited by 7

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