Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866917380260626432 |
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| author | Faye, Géraud Icard, Benjamin Casanova, Morgane Gadek, Guillaume Gravier, Guillaume Ouerdane, Wassila Hudelot, Céline Gatepaille, Sylvain Égré, Paul |
| author_facet | Faye, Géraud Icard, Benjamin Casanova, Morgane Gadek, Guillaume Gravier, Guillaume Ouerdane, Wassila Hudelot, Céline Gatepaille, Sylvain Égré, Paul |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01936 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification Faye, Géraud Icard, Benjamin Casanova, Morgane Gadek, Guillaume Gravier, Guillaume Ouerdane, Wassila Hudelot, Céline Gatepaille, Sylvain Égré, Paul Computation and Language Artificial Intelligence 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 |
| title | Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.01936 |