对比规则模型与深度学习在政治偏见识别中的表现
Balancing Transparency and Accuracy: A Comparative Analysis of Rule-Based and Deep Learning Models in Political Bias Classification
- 用语言学规则构建透明的偏见识别模型
- 规则模型在新数据上准确率更稳定，达78%
- 适合关注可解释性的政策分析与媒体研究
数字信息的无序传播，叠加日益加剧的政治极化以及个体对对立观点的回避倾向，促使研究者开发自动检测媒体政治偏见的系统。该趋势因社交媒体讨论而进一步加速。本文探讨美国新闻文章偏见分类方法，比较基于规则与深度学习两种模型。将两者应用于左倾（CNN）和右倾（FOX）新闻文章，评估其在原始训练集和测试集之外数据上的表现。结果表明，现代自学习系统对未受控数据输入敏感，而传统规则模型仍具优势。规则模型在不同数据条件下表现一致，准确率达78%，且具有更高透明性；深度学习模型依赖训练集，在未见数据上表现下降，且模型内部机制不透明。本研究为深度学习可解释性提供了分析框架，并揭示了美国新闻媒体中的政治偏见现象。
原文摘要 · Abstract (English)
The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore methods for categorizing bias in US news articles, comparing rule-based and deep learning approaches. The study highlights the sensitivity of modern self-learning systems to unconstrained data ingestion, while reconsidering the strengths of traditional rule-based systems. Applying both models to left-leaning (CNN) and right-leaning (FOX) news articles, we assess their effectiveness on data beyond the original training and test sets.This analysis highlights each model's accuracy, offers a framework for exploring deep-learning explainability, and sheds light on political bias in US news media. We contrast the opaque architecture of a deep learning model with the transparency of a linguistically informed rule-based model, showing that the rule-based model performs consistently across different data conditions and offers greater transparency, whereas the deep learning model is dependent on the training set and struggles with unseen data.
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