Subjective Bias Detection and Analysis in English Political News

Abstract

This study investigates subjective bias in English political news by combining corpus linguistics and critical discourse analysis methodologies. While news reporting is expected to be neutral, it is often influenced by journalists’ perspectives and word choices, resulting in intentional or unconscious biases. Focusing on English political news, this research systematically analyzes how linguistic choices reflect biases and affect readers’ perceptions. This study proceeds in three stages: (1) identifying topics in English political news using BERTopic, a data-driven topic modeling tool that minimizes researcher subjectivity, (2) uncovering deeper linguistic patterns and bias markers through a BERT-based bias neutralization model (Pryzant et al., 2020), and (3) applying Fairclough’s three-dimensional framework to analyze the sociocultural, political, and ideological forces shaping media discourse. This integrated methodology allows for both quantitative precision and contextual interpretation.

Findings reveal distinct patterns in how different news outlets frame similar topics, with national ideologies and editorial stances influencing linguistic choices and citation strategies. Analysis of news outlets like The Jerusalem Post and Tehran Times highlights significant biases driven by national ideologies, while comparisons between Express and The Guardian reveal different editorial stances. Further analysis of The New York Times and The Washington Post uncovers differences in language and framing despite similar levels of bias. These results demonstrate how news media outlets construct narratives aligned with their ideological and institutional contexts, emphasizing the importance of language in shaping public perceptions.