# Newsalyze: Enabling News Consumers to Understand Media Bias

arXiv: https://arxiv.org/abs/2105.09672 · HTML: https://mlsanity.com/paper/2105.09672/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2021.

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

- [Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions](https://mlsanity.com/paper/2302.08997/) (2023)
- [Experiments in News Bias Detection with Pre-Trained Neural Transformers](https://mlsanity.com/paper/2406.09938/) (2024)
- [Newsalyze: Effective Communication of Person-Targeting Biases in News Articles](https://mlsanity.com/paper/2110.09158/) (2021)
- [BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy](https://mlsanity.com/paper/2407.10829/) (2024)

## Abstract (arXiv)

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).