Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification

TL;DR

This paper introduces a neurosymbolic model combining text embeddings and symbolic features to improve the robustness of propaganda detection in news articles.

Contribution

It presents a novel hybrid approach that enhances classification accuracy and generalization by integrating genre, topic, and persuasion techniques with text embeddings.

Findings

Improved robustness over text-only models.

Ablation studies confirm the importance of symbolic features.

Explainability analyses demonstrate model interpretability.

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. Keywords: Information disorder, Fake news, Propaganda, Classification, Topic modeling, Hybrid method, Neurosymbolic model, Ablation, Robustness