Newsalyze: Effective Communication of Person-Targeting Biases in News Articles

Abstract

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.

Developing a Natural Language Understanding Model to Characterize Cable News Bias

“…Building off of this concept of press favorability, one combination of sentiment with framing was done by analyzing the affective scores of political news articles in relation to the partisan lean of the political figures they mention [30]. This model allowed for bias identification that improved user bias awareness in an experimental setting.…”