Analysing underpinning patterns in social media posts that promote fat stigmatisation : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D) in Information Technology, Massey University, Auckland, New Zealand

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dc.confidentialEmbargo : Noen_US
dc.contributor.advisorMathrani, Anuradha
dc.contributor.authorWanniarachchi, Vajisha Udayangi
dc.date.accessioned2023-01-24T02:11:41Z
dc.date.accessioned2023-03-16T02:55:41Z
dc.date.available2023-01-24T02:11:41Z
dc.date.available2023-03-16T02:55:41Z
dc.date.issued2022
dc.descriptionListed in 2023 Dean's List of Exceptional Thesesen
dc.description.abstractSocial media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain communities, this could instigate hatred towards them. With the global increase in obese/fat populations, social media discourses laced with fat hatred have become commonplace, leading to much fat stigmatising content being posted online. This research aims to investigate the patterns of fat stigma, and how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. To achieve this objective, a methodological framework is proposed for unearthing underlying stigmatising patterns prevalent in social media discussions, with specific focus on fat stigma. Methods incorporating natural language processing techniques such as sentiment analysis and topic modelling, along with discourse analysis have been described for classifying users’ emotions and comprehending the stigma patterns embedded in social big data. The framework has been applied to weight-based textual data, extracted from Twitter and Reddit, to identify emergent gender-based themes, emotions and word frequency patterns that underpin the fat stigmatising content posted online. The experiential consequences of being considered fat across both genders have been analysed with objectification theory. The findings from this study have provided a holistic outlook on fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive social media 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.en_US
dc.identifier.urihttp://hdl.handle.net/10179/18095
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectObesityen
dc.subjectSocial aspectsen
dc.subjectPhysicalen
dc.subjectappearanceen
dc.subjectbaseden
dc.subjectbiasen
dc.subjectStigma (Social psychology)en
dc.subjectSocial mediaen
dc.subjectData processingen
dc.subjectContent analysis (Communication)en
dc.subjectPhysical-appearance-based biasen
dc.subjectDean's List of Exceptional Thesesen
dc.subject.anzsrc460308 Pattern recognitionen
dc.titleAnalysing underpinning patterns in social media posts that promote fat stigmatisation : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D) in Information Technology, Massey University, Auckland, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorWanniarachchi, Vajisha Udayangien_US
thesis.degree.disciplineInformation Technologyen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.nameDoctor of Philosophy (PhD)en_US
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