Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.

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自动识别新闻报道中的媒体偏见：跨学科文献综述

媒体的偏见，即倾斜的新闻报道，会严重影响公众对报道主题的看法。在社会科学领域，过去几十年来的研究已经开发出了全面的模型来描述媒体的偏见以及有效的，但往往是手动的，因而繁琐的分析方法。相反，在计算机科学中，可以使用快速，自动化和可伸缩的方法，但是很少有系统地分析媒体偏见的方法。与在社会科学中建立的模型相比，用于分析计算机科学中的媒体偏见的模型往往更简单，并且尽管在技术上具有优势，但不一定解决最紧迫的实质性问题。因此，有关媒体偏见的计算机科学研究将受益于社会科学中开发的媒体偏见研究模型与计算机科学的自动方法之间的紧密集成。本文首先通过将社会科学的最新状态映射到框架来建立共享的概念性理解，该框架可以通过计算机科学的方法来实现。接下来，我们研究媒体偏见的不同形式，并回顾社会科学中如何分析每种形式。对于每种形式，我们然后讨论适用于（半）自动化相应分析的计算机科学方法。我们的评论表明，主要针对自然语言处理领域的计算机科学提供的合适的自动化方法已经可以用于每种讨论的媒体偏见形式，