The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias

TL;DR

The paper tackles the problem of fragmented definitions and methods in media bias research by introducing the Media Bias Taxonomy to unify concepts across disciplines. It conducts a systematic literature review of 3140 CS papers from 2019–2022, identifying transformer-based classification as the leading approach while acknowledging gaps in interdisciplinarity and bias typology. It provides a comprehensive overview of methods (traditional NLP, ML, graph-based, language-model bias) and 123 datasets, and discusses social-science perspectives on perception and mitigation. The work serves as a foundation for more rigorous, cross-domain evaluation and calls for integrating recent ML advances with diverse, well-annotated bias assessments to advance practical bias detection tools.

Abstract

The way the media presents events can significantly affect public perception, which in turn can alter people's beliefs and views. Media bias describes a one-sided or polarizing perspective on a topic. This article summarizes the research on computational methods to detect media bias by systematically reviewing 3140 research papers published between 2019 and 2022. To structure our review and support a mutual understanding of bias across research domains, we introduce the Media Bias Taxonomy, which provides a coherent overview of the current state of research on media bias from different perspectives. We show that media bias detection is a highly active research field, in which transformer-based classification approaches have led to significant improvements in recent years. These improvements include higher classification accuracy and the ability to detect more fine-granular types of bias. However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems. Concluding from our analysis, we see the integration of recent machine learning advancements with reliable and diverse bias assessment strategies from other research areas as the most promising area for future research contributions in the field.

Figures (5)

• Figure 1: Number of publications at each step of the literature retrieval and review of computer science publications.
• Figure 2: Number of publications at each step of the literature retrieval and review for the Media Bias Taxonomy.
• Figure 3: The Media Bias Taxonomy. The four subcategories of media bias consist of different bias types.
• Figure 4: Classification of computer science methods for media bias detection we use in our analysis.
• Figure 5: Overview of relevant publications per category and year. Publications can appear in multiple categories.