  News is a central source of information for individuals to inform themselves
on current topics. Knowing a news article's slant and authenticity is of
crucial importance in times of "fake news," news bots, and centralization of
media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a
subtle, yet powerful form of media bias, named bias by word choice and labeling
(WCL). WCL bias can alter the assessment of entities reported in the news,
e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a
neural model that uses a news-adapted BERT language model to determine
target-dependent sentiment, a high-level effect of WCL bias. While the analysis
currently focuses on only this form of bias, the visualizations already reveal
patterns of bias when contrasting articles (overview) and in-text instances of
bias (article view).
