# A Bias Aware News Recommendation System

arXiv: https://arxiv.org/abs/1803.03428 · HTML: https://mlsanity.com/paper/1803.03428/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL](https://mlsanity.com/paper/2503.03654/) (2025)
- [Content-Agnostic Moderation for Stance-Neutral Recommendation](https://mlsanity.com/paper/2405.18941/) (2024)
- [Text and author-level political inference using heterogeneous knowledge representations](https://mlsanity.com/paper/2206.12293/) (2022)
- [NewsLens: A Multi-Agent Framework for Adversarial News Bias Navigation](https://mlsanity.com/paper/2605.17364/) (2026)

## Abstract (arXiv)

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.