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Fat stigma and body objectification: A text analysis approach using social media content
Vajisha U Wanniarachchi1, Chris Scogings1, Teo Susnjak1
1School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand.
Digital Health
|August 22, 2022
Summary
Social media discussions often stigmatize weight, with gendered differences in how body objectification manifests. This research analyzed online content to understand these fat-stigmatizing discourses and their impact.
Area of Science:
- Sociology of Media
- Gender Studies
- Linguistic Analysis
Background:
- Social media platforms host significant discourse surrounding body weight and size.
- Existing research indicates prevalent fat-stigmatizing content online.
- Understanding gendered perspectives within these discourses is crucial.
Purpose of the Study:
- To investigate the positioning of female and male genders in fat-stigmatizing discourses on social media.
- To identify gender-based differences in body objectification themes within online weight-based content.
- To analyze the experiential consequences of weight stigma across genders using objectification theory.
Main Methods:
- A mixed-method analysis was applied to a weight-based linguistic data corpus from Twitter, YouTube, and Reddit.
- Sentiment analysis, word co-occurrences, and qualitative analysis were employed.
- Objectification theory served as the analytical framework.
Main Results:
- Five key objectifying themes emerged: attractiveness, physical appearance, lifestyle choices, health, and psychological well-being.
- Analysis revealed distinct gender-based differences in online fat-stigmatizing content.
- Both positive and negative attitudes towards obesity were identified, reflecting gendered body objectification.
Conclusions:
- Online weight/fat-stigmatizing content exhibits gendered patterns of body objectification.
- Findings provide a holistic view of online weight stigma, informing strategies for more inclusive social media spaces.
- Lexical analytics combined with data mining offers valuable insights into subject-related themes.
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