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Fat stigma and body objectification: A text analysis approach using social media content
This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising co...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386857/ https://www.ncbi.nlm.nih.gov/pubmed/35990109 http://dx.doi.org/10.1177/20552076221117404 |
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author | Wanniarachchi, Vajisha U Scogings, Chris Susnjak, Teo Mathrani, Anuradha |
author_facet | Wanniarachchi, Vajisha U Scogings, Chris Susnjak, Teo Mathrani, Anuradha |
author_sort | Wanniarachchi, Vajisha U |
collection | PubMed |
description | This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications. |
format | Online Article Text |
id | pubmed-9386857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-93868572022-08-19 Fat stigma and body objectification: A text analysis approach using social media content Wanniarachchi, Vajisha U Scogings, Chris Susnjak, Teo Mathrani, Anuradha Digit Health Original Research This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications. SAGE Publications 2022-08-15 /pmc/articles/PMC9386857/ /pubmed/35990109 http://dx.doi.org/10.1177/20552076221117404 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Wanniarachchi, Vajisha U Scogings, Chris Susnjak, Teo Mathrani, Anuradha Fat stigma and body objectification: A text analysis approach using social media content |
title | Fat stigma and body objectification: A text analysis approach using social media content |
title_full | Fat stigma and body objectification: A text analysis approach using social media content |
title_fullStr | Fat stigma and body objectification: A text analysis approach using social media content |
title_full_unstemmed | Fat stigma and body objectification: A text analysis approach using social media content |
title_short | Fat stigma and body objectification: A text analysis approach using social media content |
title_sort | fat stigma and body objectification: a text analysis approach using social media content |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386857/ https://www.ncbi.nlm.nih.gov/pubmed/35990109 http://dx.doi.org/10.1177/20552076221117404 |
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