  In this era of fake news and political polarization, it is desirable to have
a system to enable users to access balanced news content. Current solutions
focus on top down, server based approaches to decide whether a news article is
fake or biased, and display only trusted news to the end users. In this paper,
we follow a different approach to help the users make informed choices about
which news they want to read, making users aware in real time of the bias in
news articles they were browsing and recommending news articles from other
sources on the same topic with different levels of bias. We use a recent Pew
research report to collect news sources that readers with varying political
inclinations prefer to read. We then scrape news articles on a variety of
topics from these varied news sources. After this, we perform clustering to
find similar topics of the articles, as well as calculate a bias score for each
article. For a news article the user is currently reading, we display the bias
score and also display other articles on the same topic, out of the previously
collected articles, from different news sources. This we present to the user.
This approach, we hope, would make it possible for users to access more
balanced articles on given news topics. We present the implementation details
of the system along with some preliminary results on news articles.
