Fat stigma and body objectification: A text analysis approach using social media content

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dc.citation.volume8
dc.contributor.authorWanniarachchi V
dc.contributor.authorScogings C
dc.contributor.authorSusnjak T
dc.contributor.authorMathrani A
dc.coverage.spatialUnited States
dc.date.available2022
dc.date.available2022-07-15
dc.date.issued15/08/2022
dc.description.abstractThis 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.
dc.description.publication-statusPublished online
dc.format.extent20552076221117404 - ?
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/35990109
dc.identifier10.1177_20552076221117404
dc.identifier.citationDigit Health, 2022, 8 pp. 20552076221117404 - ?
dc.identifier.doi10.1177/20552076221117404
dc.identifier.elements-id455353
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2055-2076
dc.identifier.urihttps://hdl.handle.net/10179/17495
dc.languageeng
dc.publisherSAGE Publications
dc.relation.isPartOfDigit Health
dc.relation.urihttps://journals.sagepub.com/doi/10.1177/20552076221117404
dc.subjectObesity
dc.subjectfat stigma
dc.subjectgender objectification
dc.subjectmixed methods
dc.subjectsentiments
dc.subjectsocial media
dc.titleFat stigma and body objectification: A text analysis approach using social media content
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/PVC's Office - College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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