Browsing by Author "Scogings C"

Now showing 1 - 6 of 6
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    Fat stigma and body objectification: A text analysis approach using social media content
    (SAGE Publications, 15/08/2022) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
    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.
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    Gender Diversity Population Simulations in an Extended Game of Life Context
    (IEEE, 20/06/2019) Mathrani A; Scogings C; Mathrani S
    Cellular automata studies have been instrumental in computational and biological studies for simulating life contours based on simple rule-based strategies. Game of Life (GoL) presented us with one of the earliest automata studies that led the way in exemplifying non-linear spatial representations, such as large-scale population evolution scenarios depicting species dominance, species equilibrium, and species extinction. However, the GoL was driven by interactions among vegetative entities comprising live and die states only. This paper extends GoL to gendered-GoL (g-GoL) in which male phenotypes and female phenotypes interact in an extended world to procreate. Using the g-GoL, we have demonstrated many evolution contours by applying gender-based dependence rules. Evolution scenarios have been simulated with skewed gender ratios that favor the birth of male offspring. Preference for a male child is common in certain cultures; therefore, empirical data realized with skewed gender settings in g-GoL can reveal the long-term impact of non-egalitarian gender societal structures. Our model provides a tool for the study of emergent life contours and brings awareness on current gender imbalances to strengthen multi-disciplinary research inquiry in the areas of social practices, mathematical modeling, and use of computational technologies.
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    Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis
    (Australasian Association for Information Systems and Australian Computer Society, 8/02/2023) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
    Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.
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    Interpreting academic integrity transgressions among learning communities
    (BioMed Central Limited, 2021-12) Mathrani A; Han B; Mathrani S; Jha M; Scogings C
    Educational institutions rely on academic citizenship behaviors to construct knowledge in a responsible manner. However, they often struggle to contain the unlawful reuse of knowledge (or academic citizenship transgressions) by some learning communities. This study draws upon secondary data from two televised episodes describing contract cheating (or ghostwriting) practices prevalent among international student communities. Against this background, we have investigated emergent teaching and learning structures that have been extended to formal and informal spaces with the use of mediating technologies. Learners’ interactions in formal spaces are influenced by ongoing informal social experiences within a shared cultural context to influence learners’ agency. Building upon existing theories, we have developed an analytical lens to understand the rationale behind cheating behaviors. Citizenship behaviors are based on individual and collective perceptions of what constitutes as acceptable or unacceptable behavior. That is, learners who are low in motivation and are less engaged with learning may collude; more so, if cheating is not condemned by members belonging to their informal social spaces. Our analytical lens describes institutional, cultural, technological, social and behavioral contexts that influence learner agency.
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    Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review
    (MDPI (Basel, Switzerland), 2022-05-17) Wanniarachchi V; Mathrani A; Susnjak T; Scogings C; Moreno, A
    With 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.
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    Online Tracking: When Does it Become Stalking?
    (World Scientific Publishing, 2021-05-25) Amarasekara B; Mathrani A; Scogings C
    Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-tra±c generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by tra±c generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the e±cacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that de¯ne the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.