用风格、话题和修辞手法提升新闻真伪识别的鲁棒性。
Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification
- 结合fastText与符号化特征（风格/话题/修辞）进行混合建模。
- 在多个数据集上比纯文本模型准确率提升5%-8%。
- 适合关注虚假信息检测与可解释性分析的研究者。
在各类新闻乱象中，宣传性新闻尤为隐蔽，常混入事实报道以伪装成可靠信息。现有基于BERT等语言模型的方法虽有潜力，但因数据收集偏差易过拟合训练集。为提升分类鲁棒性并增强对新来源的泛化能力，本文提出一种神经符号方法，融合非上下文文本嵌入（fastText）与符号化概念特征，包括新闻类型、主题及修辞技巧。实验结果表明，该方法优于同等条件下的纯文本模型；消融实验与可解释性分析进一步验证了新增特征的有效性。
原文摘要 · Abstract (English)
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
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