Examining Stereotypes in News Articles

Abstract

Gender biases or stereotypes have been studied in short text and manually labeled corpora, but little work has been done in real-world unlabeled text corpora like news articles. This work investigated news articles from mainstream U.S. media outlets ranging from 2013 to early 2020. We used structural topic modeling to estimate gender prevalent topics, compared the results with topic modeling embedding, and incorporated qualitative and quantitative analyses to understand the appearance of gender stereotypes in news articles from each gender group. The structural topic modeling results showed that gender prevalent topics align with stereotypical representations of either gender group and media outlets with imbalanced gender distribution are more influential on stereotypical representations. The topic modeling embedding results support prior results and provide additional information supporting the conclusion.