Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias

TL;DR

This paper presents a comprehensive methodology combining large language models, topic modeling, sentiment analysis, and ontology learning to analyze media bias in political news, aiming for scalable and minimally biased detection.

Contribution

It introduces a novel, integrated approach for media bias analysis using advanced NLP techniques, enhancing scalability and granularity over prior methods.

Findings

Effective identification of biases across news sources.

Demonstrated methodology on three political case studies.

Lays groundwork for bias detection tools for consumers.

Abstract

Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed approach examines event selection, labeling, word choice, and commission and omission biases across news sources by leveraging natural language processing techniques, including hierarchical topic modeling, sentiment analysis, and ontology learning with large language models. Through three case studies related to current political events, we demonstrate the methodology's effectiveness in identifying biases across news sources at various levels of granularity. This work represents a significant step towards scalable, minimally biased media bias analysis, laying the groundwork for tools to help news consumers navigate an increasingly complex media landscape.