Publications and Research
Document Type
Article
Publication Date
Summer 5-6-2026
Abstract
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.
Included in
Artificial Intelligence and Robotics Commons, Computational Linguistics Commons, Data Science Commons, Social Influence and Political Communication Commons
Comments
Published in: Proceedings of the Thirty-Ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS-39), Special Track: Applied Natural Language Processing.
Presentation Status: Accepted for oral presentation at FLAIRS-39 (Special Track: Applied Natural Language Processing), May 18, 2026.
DOI: https://doi.org/10.32473/flairs.39.1.141595
Authors:
Citation:
Kimmeth, T., Roy, A., & Sharma, V. (2026). Propasafe-Hybrid: A Text-Based Hybrid Propaganda Detection Tool. The International FLAIRS Conference Proceedings, 39(1). https://doi.org/10.32473/flairs.39.1.141595
Open access article published under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).