English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Gender and Prestige Bias in Coronavirus News Reporting