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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
