Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases can be best visualized and communicated. We create three manually annotated datasets and test varying visualization strategies. The results show no strong effects of becoming aware of the bias of the treatment groups compared to the control group, although a visualization of hand-annotated bias communicated bias instances more effectively than a framing visualization. Showing participants an overview page, which opposes different viewpoints on the same topic, does not yield differences in respondents' bias perception. Using a multilevel model, we find that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article.
翻译:传统媒体以偏颇的方式报道政治新闻,可能会影响听众的政治信仰,甚至改变他们的投票行为。许多研究人员侧重于自动发现和识别新闻中的媒体偏见,但只有极少数研究系统地分析这些偏见如何能最好地视觉化和传播。我们手工制作了三个附加注释的数据集,测试了不同的视觉化战略。结果显示,人们没有意识到治疗群体与控制群体相比的偏见的强烈影响,尽管手语附加注释的偏见的视觉化比框架化的视觉化更能有效地传播偏见事件。向参与者展示一个反对同一主题不同观点的概览页并不在受访者的偏见观念上产生差异。我们用多层次的模式发现,人们所感觉到的记者偏见与认为文章的政治极端性和公正性密切相关。