Gender and Prestige Bias in Coronavirus News Reporting

TL;DR

This study uses NLP to analyze biases in Covid-19 news reporting, revealing significant gender and prestige disparities in expert quotations, with conservative media showing greater gender bias and a preference for prestigious institutions.

Contribution

The paper introduces a novel NLP-based methodology to quantify gender and prestige biases in news coverage, highlighting disparities in expert representation during the pandemic.

Findings

Men are quoted three times more than women.

Conservative media exhibit greater gender bias.

Experts from prestigious institutions are favored over more relevant but less prestigious ones.

Abstract

Journalists play a vital role in surfacing issues of societal importance, but their choices of what to highlight and who to interview are influenced by societal biases. In this work, we use natural language processing tools to measure these biases in a large corpus of news articles about the Covid-19 pandemic. Specifically, we identify when experts are quoted in news and extract their names and institutional affiliations. We enrich the data by classifying each expert's gender, the type of organization they belong to, and for academic institutions, their ranking. Our analysis reveals disparities in the representation of experts in news. We find a substantial gender gap, where men are quoted three times more than women. The gender gap varies by partisanship of the news source, with conservative media exhibiting greater gender bias. We also identify academic prestige bias, where journalists turn to experts from highly-ranked academic institutions more than experts from less prestigious institutions, even if the latter group has more public health expertise. Liberal news sources exhibit slightly more prestige bias than conservative sources. Equality of representation is essential to enable voices from all groups to be heard. By auditing bias, our methods help identify blind spots in news coverage.