PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent
Not on the map. Why?
Abstract (arXiv)
Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce propainsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. propainsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present propagaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but training with propagaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span IoU in technique identification and 66.2% higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, propagaze complements limited human-annotated data in data-sparse and cross-domain scenarios, showing its potential for comprehensive and generalizable propaganda analysis.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 27 of its 73 references that are in the map. First posted on arXiv 19 Sep 2024.
Builds on
27 of 73 references.
References · 27
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2019Defending Against Neural Fake Newscited by 553
- 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 293
- 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 281
- 2023A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Modelscited by 244
- 2019Fine-Grained Analysis of Propaganda in News Articlescited by 130
- 2020SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articlescited by 79
- 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 77
- 2023Word Embeddings Are Steers for Language Modelscited by 70
- 2020A Survey on Computational Propaganda Detectioncited by 69
- 2023LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Modelscited by 58
15 more
- 2024Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinementcited by 47
- 2022Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generationcited by 41
- 2023LeTI: Learning to Generate from Textual Interactionscited by 25
- 2020Prta: A System to Support the Analysis of Propaganda Techniques in the Newscited by 23
- 2021Interpretable Propaganda Detection in News Articlescited by 16
- 2024EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundariescited by 16
- 2022Russian propaganda on social media during the 2022 invasion of Ukrainecited by 15
- 2023Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecastingcited by 11
- 2023SmartBook: AI-Assisted Situation Report Generation for Intelligence Analystscited by 9
- 2023Measuring the Effect of Influential Messages on Varying Personascited by 8
- 2023Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Studycited by 5
- 2023Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognitioncited by 5
- 2019Neural Architectures for Fine-Grained Propaganda Detection in Newscited by 4
- 2020Characterizing networks of propaganda on Twitter: a case studycited by 4
- 2022A Weibo Dataset for the 2022 Russo-Ukrainian Crisiscited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 4; 2026: 4.
9 papers.
- 2025Must Read: A Comprehensive Survey of Computational Persuasioncited by 12
- 2025CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answeringcited by 10
- 2025TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Lawcited by 7
- 2024ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?cited by 5
- 2025A Review of Incorporating Psychological Theories in LLMscited by 5
- 2026IntentGrasp: A Comprehensive Benchmark for Intent Understandingcited by 1
- 2026SURE: Synergistic Uncertainty-aware Reasoning for Multimodal Emotion Recognition in Conversationscited by 0
- 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0
- 2026Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemascited by 0
- 2026Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas13 May 2026
- 2026IntentGrasp: A Comprehensive Benchmark for Intent Understanding7 May 2026
- 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathy12 Apr 2026
- 2026SURE: Synergistic Uncertainty-aware Reasoning for Multimodal Emotion Recognition in Conversations2 Apr 2026
- 2025TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law22 Jul 2025
- 2025Must Read: A Comprehensive Survey of Computational Persuasion12 May 2025
- 2025A Review of Incorporating Psychological Theories in LLMs28 Mar 2025
- 2025CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering30 Jan 2025
- 2024ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?18 Nov 2024