Fine-Grained Analysis of Diversity Levels in the News

• In Event: Get the Balance Right: Exploring Media Bias and Media Narratives

Abstract

Diverse content is considered by many researchers as a prerequisite for the news media to fully exercise its democratic mandate. Yet scholarly knowledge of news diversity has been limited in two main ways. Conceptually, prior research employs crude diversity measures focusing on small sets of predefined political categories, such as broad issue domains. Methodologically, the reliance of prior studies on human-based content analysis limits the volume of data that can be analyzed and the resolution of the analysis. To address these limitations, we develop a novel framework for analyzing news diversity. Using a combination of unsupervised and supervised computational methods, our approach (1) inductively identifies comprehensive sets of political issues and sub-issues that appear regularly in the news and (2) analyzes media attention to these topics at high resolution and precision. After presenting our approach, we illustrate its usefulness for testing theoretical hypotheses by comparing the diversity of economic coverage in three elite and three popular newspapers (N = 252,807 news articles). We find that a fine-grained analysis relaxes prior concerns about low diversity levels in popular outlets. More broadly, our study advances scholarly understanding of the quality of media content and underscores the need to ask more nuanced questions about what issues, actors, and viewpoints appear in the news.

Authors

• Eran Amsalem, The Hebrew University of Jerusalem

• Yair Fogel-Dror, The Hebrew University of Jerusalem

• Shaul Shenhav, Hebrew University of Jerusalem

• Tamir Sheafer, Hebrew University of Jerusalem