Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
Year: 2026
Type: conference-paper
Abstract: Among news disorders, propagandist news are particularly insidious, because they tend to mix oriented messages with factual reports intended to look like reliable news.To detect propaganda, extant approaches based on Language Models such as BERT are promising but often overfit their training datasets, due to biases in data collection.To enhance classification robustness and improve generalization to new sources, we propose a neurosymbolic approach combining non-contextual text embeddings (fastText) with symbolic conceptual features such as genre, topic, and persuasion techniques.Results show improvements over equivalent text-only methods, and ablation studies as well as explainability analyses confirm the benefits of the added features.
Institutions: Airbus (France), Université Paris-Saclay, CentraleSupélec, Centre National de la Recherche Scientifique, Sorbonne Université, LIP6, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes, French–Australian Laboratory for Human / Autonomous Agents Teaming
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166514828