The manifestation and effect of bias in news reporting have been central
topics in the social sciences for decades, and have received increasing
attention in the NLP community recently. While NLP can help to scale up
analyses or contribute automatic procedures to investigate the impact of biased
news in society, we argue that methodologies that are currently dominant fall
short of addressing the complex questions and effects addressed in theoretical
media studies. In this survey paper, we review social science approaches and
draw a comparison with typical task formulations, methods, and evaluation
metrics used in the analysis of media bias in NLP. We discuss open questions
and suggest possible directions to close identified gaps between theory and
predictive models, and their evaluation. These include model transparency,
considering document-external information, and cross-document reasoning rather
than single-label assignment.

新闻报道中的偏见表现和影响是社会科学的核心主题，近年来在自然语言处理领域受到了越来越多的关注。本研究综述了社会科学的方法，并将其与自然语言处理领域中用于分析媒体偏见的典型任务表述、方法和评估指标进行了对比。我们讨论了开放性问题，并提出了可能的研究方向，以填补理论与预测模型之间及其评估之间的差距。这些包括模型透明度、考虑文档外部信息以及跨文档推理而非单一标签的分配。