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.

翻译：媒体呈现事件的方式会显著影响公众认知，进而改变人们的信念与观点。媒体偏见描述了对某一话题的片面或极化视角。本文通过系统梳理2019至2022年间发表的3140篇研究论文，总结了检测媒体偏见的计算方法研究现状。为构建综述框架并促进跨研究领域对偏见的共同理解，我们提出了媒体偏见分类体系（Media Bias Taxonomy），该体系从不同视角系统整合了当前媒体偏见研究的全景图。研究表明，媒体偏见检测是一个高度活跃的研究领域，其中基于Transformer的分类方法近年来取得了显著进展，包括分类精度的提升以及更细粒度偏见类型的识别能力。然而，现有项目存在跨学科性不足的问题，且需要加强对各类媒体偏见的认知以支撑媒体偏见检测系统的方法论严谨性评估。基于分析结论，我们认为将机器学习最新进展与其他研究领域可靠且多元的偏见评估策略相融合，是该领域最具前景的未来研究方向。