新闻分析中的点线连接：弥合媒体偏见与框架研究中的跨学科差异
Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing
AI总结：
该综述对比了社会科学与NLP领域的媒体偏见分析方法，指出当前NLP方法的不足，提出了弥合理论与模型及评估差距的多个方向。
摘要：
数十年来，新闻报道中偏见的表现形式及影响一直是社会科学的核心议题，近年来也受到NLP（自然语言处理）领域越来越多的关注。尽管NLP能够助力扩大分析规模，或提供自动化流程以研究偏见新闻对社会的影响，但我们认为，当前主流的研究方法不足以应对理论媒体研究中提出的复杂问题与效应。在这篇综述论文中，我们梳理了社会科学的研究方法，并将其与NLP领域媒体偏见分析中常用的典型任务设定、方法及评估指标进行了对比。我们探讨了尚未解决的问题，并提出了缩小理论与预测模型及其评估之间已识别差距的可能方向，这些方向包括模型透明度、纳入文档外部信息，以及采用跨文档推理而非单标签分配的方式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
