Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
This addresses the challenge of robust propaganda detection for media and fact-checking applications, but it is incremental as it builds on existing methods by adding symbolic features.
The paper tackled the problem of detecting propagandist news by proposing a neurosymbolic model that combines non-contextual text embeddings with symbolic features like genre, topic, and persuasion techniques to improve robustness and generalization over text-only methods, with results showing improvements.
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. Keywords: Information disorder, Fake news, Propaganda, Classification, Topic modeling, Hybrid method, Neurosymbolic model, Ablation, Robustness