  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.
