How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?
This addresses the issue of biased news influencing public opinion, particularly in politics, by providing an automated tool for bias detection, though it is incremental as it builds on existing content analysis methods.
The paper tackles the problem of identifying person-targeting media bias in news articles by automating content analysis, and finds that recommending articles with different frames significantly improves users' bias awareness in a large-scale study.
Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.