Balancing Transparency and Accuracy: A Comparative Analysis of Rule-Based and Deep Learning Models in Political Bias Classification

TL;DR

This paper compares rule-based and deep learning models for detecting political bias in US news articles, highlighting their strengths, weaknesses, and transparency differences in real-world data scenarios.

Contribution

It provides a comparative analysis of traditional rule-based and modern deep learning approaches for political bias classification, emphasizing transparency and robustness.

Findings

Rule-based models are more transparent and consistent across data sets.

Deep learning models are sensitive to data variations and less explainable.

Traditional rule-based systems outperform deep learning in unseen data scenarios.

Abstract

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.