Visualizing Polarity-based Stances of News Websites

We develop a novel framework that helps identify potential bias in news websites to support users who are exposed to news articles with a wide variety of political leanings. We propose a polarity-based stance (PS), a vector that represents how often a website publishes articles that are positive or negative with regard to a topic. We derive PS using the GDELT database and visualize the news websites’ stances. We demonstrate the utility of our framework via a case study of the 2016 US Presidential Election.