如何有效识别和沟通日常新闻消费中的个人目标媒体偏见？
How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?
摘要：
有倾向性的新闻报道强烈地影响公众舆论。政治和相关问题的报道尤其如此，研究表明，新闻中的偏见可能会影响选举和其他集体决定。由于其可行的重要性，新闻报道长期以来一直在社会科学中进行研究，从而产生了描述它的综合模型和有效但昂贵的分析方法，例如内容分析。我们提出了一个正在进行的新闻推荐系统，这是第一个自动化的手动程序的内容分析，以揭示针对政策问题的新闻报道中的个人偏见。在一项大规模的用户研究中，我们发现了关于这个跨学科研究方向的非常有前途的结果。我们的推荐器检测并揭示了实际存在于个别新闻文章中的大量框架。相比之下，先前的工作只是促进了偏见的可见性，例如，通过区分左翼和右翼的出路。此外，我们的研究表明，推荐新闻文章，不同的框架事件显着提高受访者的偏见意识。
英文摘要：
Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.
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。
