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.

研究表明，媒体的政治偏见会影响受众的政治信仰及投票行为。通过三个手动注释的数据集及不同的可视化策略测试，结果表明手动注释偏见的可视化比框架可视化更有效。通过多层次模型，可以发现记者的偏见与文章的政治极端性和公正性有显著联系。