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dc.contributor.authorWanniarachchi V
dc.contributor.authorMathrani A
dc.contributor.authorSusnjak T
dc.contributor.authorScogings C
dc.contributor.editorMoreno, A
dc.date.available2022-05-17
dc.date.issued2022-05-17
dc.identifierhttps://www.mdpi.com/journal/applsci
dc.identifier5045
dc.identifier.citationApplied Sciences, 2022, 12 (10)
dc.identifier.issn2076-3417
dc.description.abstractWith increased obesity rates worldwide and the rising popularity in social media usage, we have witnessed a growth in hate speech towards fat/obese people. The severity of hate content has prompted researchers to study public perceptions that give rise to fat stigma from social media discourses. This article presents a systematic literature review of recent literature published in this domain to gauge the current state of research and identify possible research gaps. We have examined existing research (i.e., peer-reviewed articles that were systematically included using the EBSCO discovery service) to study their methodological aspects by reviewing their context, domain, analytical methods, techniques, tools, features and limitations. Our findings reveal that while recent studies have explored fat stigma content in social media, these mostly acquired manual analytical methods regardless of the evolved machine learning, natural language processing and deep learning methods. Although fat stigma in social media has gained enormous attention in current socio-psychological research, there exists a gap between how such research is conducted and what technologies are being applied, which limits in-depth investigations of fat stigma discussions.
dc.publisherMDPI, Basel, Switzerland.
dc.relation.urihttps://www.mdpi.com/2076-3417/12/10/5045
dc.rightsCC BY
dc.subjectfat stigma
dc.subjectsocial media
dc.subjectoverweight
dc.subjectobesity
dc.subjectanalytical method
dc.subjectlimitation
dc.titleMethodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review
dc.typeJournal article
dc.citation.volume12
dc.identifier.doi10.3390/app12105045
dc.description.confidentialfalse
dc.identifier.elements-id453283
dc.relation.isPartOfApplied Sciences
dc.citation.issue10
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
dc.identifier.harvestedMassey_Dark
pubs.notesNot known
dc.publisher.urihttps://www.mdpi.com/journal/applsci


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