Unraveling media perspectives: a comprehensive methodology combining large language models, topic modeling, sentiment analysis, and ontology learning to analyse media bias

Orlando Jähde (), Thorsten Weber () and Rüdiger Buchkremer ()
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Orlando Jähde: FOM University of Applied Sciences in Economics and Management
Thorsten Weber: FOM University of Applied Sciences in Economics and Management
Rüdiger Buchkremer: FOM University of Applied Sciences in Economics and Management

Journal of Computational Social Science, 2025, vol. 8, issue 2, No 15, 56 pages

Abstract: 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.

Keywords: Large language model; Machine learning; Media bias; Natural language processing; Ontology learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00372-0

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