Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms

Kaiping Chen; Zening Duan; Sang Jung Kim

doi:10.1093/jcmc/zmad052

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Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms

Journal article

Open access

Peer reviewed

Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms

Kaiping Chen, Zening Duan and Sang Jung Kim

Journal of computer-mediated communication, Vol.29(1), zmad052

02/02/2024

DOI: 10.1093/jcmc/zmad052

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Abstract

Abstract This study examines how gender stereotypes are reflected in discourses around controversial science issues across two platforms, YouTube and TikTok. Utilizing the Social Identity Model of Deindividuation Effects, we developed hypotheses and research questions about how content creators might use gender-related stereotypes to engage audiences. Our analyses of climate change and vaccination videos, considering various modalities such as captions and thumbnails, revealed that themes related to children and health often appeared in videos mentioning women, while science misinformation was more common in videos mentioning men. We observed cross-platform differences in portraying gender stereotypes. YouTube’s video descriptions often highlighted women-associated moral language, whereas TikTok emphasized men-associated moral language. YouTube’s thumbnails frequently featured climate activists or women with nature, while TikTok’s thumbnails showed women in Vlog-style selfies and with feminine gestures. These findings advance understanding about gender and science through a cross-platform, multi-modal approach and offer potential intervention strategies.

Details

Title: Subtitle: Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms
Creators: Kaiping Chen - University of Wisconsin–Madison
Zening Duan - University of Wisconsin–Madison
Sang Jung Kim - University of Iowa
Contributors: Sandra González-Bailón (Editor)
Emőke-Ágnes Horvát (Editor)
Resource Type: Journal article
Publication Details: Journal of computer-mediated communication, Vol.29(1), zmad052
DOI: 10.1093/jcmc/zmad052
ISSN: 1083-6101
eISSN: 1083-6101
Grant note: name: WARF Accelerator Big Data Challenge Grant; name: Robert F. and Jean E. Holtz Center; name: School of Journalism and Mass Communication; DOI: 10.13039/100007015, name: University of Wisconsin-Madison
Language: English
Electronic publication date: 11/08/2023
Date published: 02/02/2024
Academic Unit: Center for Social Science Innovation; School of Journalism and Mass Communication
Record Identifier: 9984556760302771

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