Propaganda Detection

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10 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool
Oct 24, 2026Thomas Kimmeth, Avijit Roy, Vivek SharmaPropaganda Detection Text Classification
John Jay College of Criminal Justice, CUNY New York, NY
Reasoning for Propaganda Detection via Label-Guided CoT Optimisation
Oct 12, 2026Zeba Afroz, Harsh Vardhan, Md. Shad AkhtarExplainable Text Classification Propaganda Detection
Indraprastha Institute of Information Technology Delhi
Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models
Oct 2, 2026Claudiu Creanga, Ioachim Lihor, Liviu P. DinuPropaganda Detection
Faculty of Mathematics and Informatics, Human Language Technologies Research Center, Interdisciplinary School of Doctoral Studies, University of Bucharest Romania
SWARM: A Multilingual Human-Annotated Dataset for Russian Propaganda Detection in Search Engine Results
Sep 11, 2026Manuel Tonneau, Abhinav Dubey, Farhan Shaikh +8Fake News Detection Text Classification
Weizenbaum Institute · University of Oxford · University of Bern
ProBel: Propaganda Detection with Techniques, Spans, and Explanations
Aug 23, 2026Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori +2Propaganda Detection Text Classification
Qatar Computing Research Institute, Qatar · University of Padova, Italy
Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign
Aug 16, 2026Benjamin Icard, Elouan Vuichard, Louis Lefebvre +12Persuasion in Language Models Propaganda Detection
LIP6, Sorbonne University, CNRS · IRL Crossing, CNRS · AIRBUS +2
NewsLens: A Multi-Agent Framework for Adversarial News Bias Navigation
May 17, 2026Joy BosePropaganda Detection Multi-Agent Systems
Independent Researcher, Bengaluru, India
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas
May 13, 2026Lukas Stähelin, Veronika Solopova, Max Upravitelev +6LLM Prompting LLM Fine-Tuning
Technische Universität Berlin, QU Lab, XplaiNLP Group, Berlin, Germany · German Research Center for Artificial Intelligence (DFKI), Berlin, Germany · Centre for European Research in Trusted AI (CERTAIN)
Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
Apr 2, 2026Géraud Faye, Benjamin Icard, Morgane Casanova +6Propaganda Detection Fake News Detection
Airbus Defence and Space, France · Université Paris-Saclay, CentraleSupélec, MICS, France · LIP6, Sorbonne Université, CNRS, France +2
Large-Scale Analysis of Persuasive Content on Moltbook
Mar 18, 2026Julia Jose, Meghna Manoj Nair, Rachel GreenstadtLarge Language Model-Based Text Classification Political Discourse Analysis
Department of Computer Science and Engineering, New York University, New York, NY, USA

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Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool

Thomas Kimmeth, Avijit Roy, Vivek Sharma

John Jay College of Criminal Justice, CUNY New York, NY

Propaganda Detection Text Classification Explainable Text Classification Large Language Model-Based Text Classification

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Propagandistic content increasingly circulates through online news and social media, where readers often encounter it with limited scrutiny, highlighting the need for reliable and fine-grained detection. This paper introduces Propasafe-Hybrid, a sentence-level system that integrates a fine-tuned transformer classifier with LLM-based technique classification to identify, label, and explain specific propaganda strategies. The pipeline generates actionable outputs, including highlighted sentences, technique assignments, and concise rationales, so users can immediately understand why a sentence was flagged and how each label was determined. To control inference cost, Propasafe-Hybrid employs a cost-aware pre-filtering stage that forwards only high-likelihood sentences to LLMs, reducing token usage while preserving the underlying decision logic. Together, these design choices enhance the explainability, efficiency, and practical usability of sentence-level propaganda detection in real-world news environments.