Results and further resources concerning our pre-studies concerning revealing biases in news articles

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 strongly 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.带有偏向性的新闻报道会对公众舆论产生强烈影响。这一点在政治及相关议题的报道中尤为突出——已有研究表明，新闻中的偏见可能会严重影响选举及其他集体决策的走向。鉴于其重大研究价值，新闻报道长期以来都是社会科学领域的研究对象，由此催生了用于描述新闻报道的全面模型，以及诸如内容分析（content analysis）这类高效却成本高昂的分析方法。本研究提出一款处于研发阶段的新闻推荐系统，它是首个可将手动内容分析流程自动化的工具，用于揭露政策议题相关新闻报道中存在的针对特定主体的偏见。在一项大规模用户研究中，我们针对这一跨学科研究方向取得了极具前景的研究成果。我们的推荐系统能够检测并揭示单篇新闻报道中实际存在的实质性报道框架。与之相比，此前的相关研究仅能提升偏见的可见性，例如通过区分左翼与右翼新闻媒体。此外，我们的研究表明，推荐以不同框架报道同一事件的新闻，可显著提升受访者对偏见的认知水平。