JOURNAL ARTICLE
What's going on down there? Subtitle sexism and gender representation in Masters of the Universe: Revelation.
Published In: Journal of Popular Television, 2025, v. 13, n. 1. P. 3 1 of 3
Database: Film & Television Literature Index with Full Text 2 of 3
Authored By: Armstrong, Alice J.; Smith, Josefine M.; Knight, Misty L. 3 of 3
Abstract
This article introduces a novel methodology for quantitatively analyzing film and television scripts using freely available subtitle files and common spreadsheet tools, demonstrated through a case study of gendered communication in the Netflix animated series *Masters of the Universe: Revelation* (2021–present). The study examines speaking time, conversational topics, and dialogue patterns—such as Bechdel–Wallace conversations and sustained monologues—to assess female representation and empowerment within the series. Findings reveal that although female characters, particularly the lead Teela, have significant speaking time, male characters often hold longer uninterrupted speaking turns, and female-to-female conversations about topics other than men are unevenly distributed, correlating with episodes featuring female head writers. The research situates these results within postfeminist and commodity feminism frameworks, suggesting that despite a female-led narrative, the series reflects ongoing patriarchal influences in media representation. The article also highlights the accessibility and potential of subtitle-based analysis for broader media studies research.
Additional Information
- Source:Journal of Popular Television. 2025/03, Vol. 13, Issue 1, p3
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:2046-9861
- DOI:10.1386/jptv_00133_1
- Accession Number:186875034
- Copyright Statement:Copyright of Journal of Popular Television is the property of Intellect Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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