A Bias Aware News Recommendation System

TL;DR

This paper introduces a bias-aware news recommendation system that informs users of bias levels in articles and suggests balanced alternatives from diverse sources, promoting informed news consumption.

Contribution

It presents a novel user-centric approach to bias awareness and balanced news recommendation, differing from traditional server-based fake news detection methods.

Findings

Implemented a bias scoring mechanism for news articles

Displayed bias scores and balanced alternatives to users in real-time

Preliminary results show potential for promoting balanced news access

Abstract

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.