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.
翻译：剪切式新闻报道对舆论有很大影响。对政治和相关问题的报道来说尤其如此,因为研究表明新闻中的偏见可能会影响选举和其他集体决定。 由于其具有可行的重要性,社会科学长期以来一直在研究新闻报道,从而产生了全面描述这种报道的模式以及分析这种报道的有效但成本高的方法,例如内容分析。我们为新闻建议提供了一个进展中的新闻建议系统,这是将内容分析的手工程序自动化,以揭示报道政策问题的新闻报道中针对个人的偏见。在一项大型用户研究中,我们发现这一跨学科研究方向的极有希望的结果。我们的推荐者发现并揭示了在个别新闻文章中实际存在的大量框架。相反,先前的工作只是通过区分左翼和右翼的渠道来便利偏见的可见度。此外,我们的研究显示,建议内容分析的手工程序首次自动化,以揭示在报道政策问题的新闻报道中针对个人的偏见。在一项大型的用户研究中,我们发现这种研究方向非常有希望的结果。我们的建议者发现,发现并揭示了在个别新闻文章中实际存在的大量框架。相反,以前的研究只是通过区分左翼和右翼的渠道等手段,而只是为偏见的可见度。此外,我们的研究显示,建议新闻文章建议新闻文章有不同的框架大大提高了受访者对受访者对偏见的认识。
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