# Unmasking Bias in News

## TL;DR

This paper investigates hyperpartisanship detection in news articles, revealing that topic features outperform stylistic ones and highlighting the need for more nuanced datasets to identify subtle biases.

## Contribution

It introduces a masking method to differentiate style and content in bias detection and demonstrates the effectiveness of higher-length n-grams in current models.

## Key findings

- Topic features outperform stylistic features in bias detection
- Higher-length n-grams achieve competitive results
- More challenging datasets are needed for subtle bias detection

## Abstract

We present experiments on detecting hyperpartisanship in news using a 'masking' method that allows us to assess the role of style vs. content for the task at hand. Our results corroborate previous research on this task in that topic related features yield better results than stylistic ones. We additionally show that competitive results can be achieved by simply including higher-length n-grams, which suggests the need to develop more challenging datasets and tasks that address implicit and more subtle forms of bias.