Newsalyze: Enabling News Consumers to Understand Media Bias

TL;DR

Newsalyze is a neural-based news reader that detects subtle media bias through word choice and labeling, helping users understand bias patterns and assess news authenticity.

Contribution

It introduces a neural model using a news-adapted BERT to identify bias by word choice and labeling, focusing on target-dependent sentiment analysis.

Findings

Visualizations reveal bias patterns across articles

The model effectively detects bias in word choice and labeling

Provides insights into media bias for news consumers

Abstract

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).