Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets.
翻译：媒体偏见及其极端形式,假新闻,可以决定性地影响公众舆论。特别是当报道政策问题时,倾斜的新闻报道可能会强烈影响社会决策,例如在民主选举中。我们的论文为解决这一问题作出了三点贡献。首先,我们提出了一个偏见识别系统,将自然语言理解中最先进的方法结合起来。第二,我们设计了对偏见敏感的视觉化方法,在新闻文章中向非专家新闻消费者传播偏见。第三,我们的主要贡献是进行大规模用户研究,在接近每日新闻消费的环境中衡量偏见意识,例如,我们向受访者提供新闻概览和个别文章。我们不仅衡量可视化对受访者偏见意识的影响,而且我们还可以通过使用连带设计来确定对视觉化个别组成部分的影响。我们注重偏见的概览大大提高了受访者对偏见的认识。我们的研究进一步表明,我们以内容驱动的识别方法检测了因个别新闻文章中存在重大偏差而产生的类似剪切的新闻报道群体。相比之下,审查前的工作只是通过右面和左面来区分偏见。
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