List of Titles

EDBase : generating a lexicon base for eating disorders via social media

- Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Abuhassan, Mohammad, Shatte, Adrian, WIRED Team, Sukunesan, Suku

Authors: Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Abuhassan, Mohammad , Shatte, Adrian , WIRED Team , Sukunesan, Suku
Date: 2022
Type: Text , Journal article
Relation: IEEE Journal of Biomedical and Health Informatics Vol. 26, no. 12 (2022), p. 6116-6125
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Description: Eating disorders (EDs) are characterised by abnormal eating habits and obsessive thought about food, weight, shape, and body image. EDs are experienced by a significant portion of our population. Social media is identified as a possible source of influence for EDs, and there is growing evidence of a large amount of ED-related discussions on the Web via social media platforms, such as Twitter. With this growing trend, automatic content analysis for EDs is becoming increasingly important. To date, there does not exist any comprehensive benchmark ED lexicon to identify ED-related conversations that would, in turn, facilitate these content analysis tasks. In this paper, we propose a novel method for generating a lexicon base for ED language, called EDBase. The method starts with collecting over 3.7 million ED-focused tweets. In order to semantically represent potential ED terminology in a vector space, an ED word embedding model (EDModel) is trained. Then we develop a novel multi-seeded hierarchical density-based algorithm with contrasting corpora for ED lexicon expansion. The EDModel is queried by the proposed lexicon expansion algorithm to expand the seed terms to a comprehensive lexicon base. Our EDBase consists of a (further expandable) list of 3794 high-quality ED terms, quantified by an ED score, and linked to their parent terms. The proposed method significantly outperforms all existing alternative baseline methods and models by over 25% in terms of precision and 1500 in terms of true positives. This research is expected to be impactful in the health data science and healthcare community. © 2021 IEEE.

Classification of Twitter users with eating disorder engagement : learning from the biographies

- Abuhassan, Mohammad, Anwar, Tarique, Fuller-Tyszkiewicz, Matthew, Jarman, Hannah, Shatte, Adrian, Liu, Chengfei, Sukunesan, Suku

Authors: Abuhassan, Mohammad , Anwar, Tarique , Fuller-Tyszkiewicz, Matthew , Jarman, Hannah , Shatte, Adrian , Liu, Chengfei , Sukunesan, Suku
Date: 2023
Type: Text , Journal article
Relation: Computers in Human Behavior Vol. 140, no. (2023), p.
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Description: Individuals with an Eating Disorder (ED) are typically reluctant to seek help via traditional means (e.g., psychologists). However, recent evidence suggests that many individuals seek assistance via social media for weight and diet related concerns. Sophisticated approaches are needed to better distinguish those who may be in need of help for an ED from those who are simply commenting on ED in online social environments. In order to facilitate effective communication between individuals with or at-risk of an ED and healthcare professionals, this research exploits a deep learning model to differentiate the users with ED engagement (e.g., ED sufferers, healthcare professionals or communicators) over social media. For this purpose, a collection of Twitter data is compiled using Twitter application programming interface (API) on the Australian Research Data Commons (ARDC) Nectar research cloud. After collecting 1,400,000 Twitter biographies in total, a subset of 4000 biographies are annotated manually. This annotation enables the differentiation of users engaged with ED-focused language on social media into five categories: ED-user, healthcare professional, communicator, healthcare professional-communicator, and other. Based on these annotated categories, a predictive deep learning model based on bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is developed. The model achieves an F1 score of 98.19% and an accuracy of 98.37%. It demonstrates the viability of detecting the individuals with possible ED risk and distinguishes them from other categories using their biography data. We further conducted a network analysis for investigating the communication network between these categories. Our analysis shows that ED-users are more secretive and self-protective, whereas the healthcare professionals and communicators frequently interact with each other and a wide range of other people. To the best of our knowledge, our research is the first of its kind for identifying the different user categories engaged with ED-focused communications on social media. © 2022

Rhetoric and reality : Critical perspectives on education in a 3D virtual world

- Gregory, Sue, Gregory, Brent, Wood, Denise, Butler, Des, Pasfield-Neofitou, Sarah Ellen, Hearns, Merle, Se Freitas, Sarah, Farley, Helen, Warren, Ian, Jacka, Lisa, Stokes-Thompson, Frederick, Cox, Robert, Crowther, Patricia, Atkins, Clare, McDonald, Marcus, Reiners, Torsten, Wood, Lincoln, Sim, Jenny, Grant, Scott, Campbell, Chris, Hillier, Mathew, Meredith, Grant, Steel, Caroline, Jegathesan, Jay Jay, Zagami, Jason, Sukunesan, Suku, Gaukrodger, Belma, Schutt, Stefan, Le Rossignol, Karen, Hill, Matthew, Rive, Pete, Xiangyu, Wang

Authors: Gregory, Sue , Gregory, Brent , Wood, Denise , Butler, Des , Pasfield-Neofitou, Sarah Ellen , Hearns, Merle , Se Freitas, Sarah , Farley, Helen , Warren, Ian , Jacka, Lisa , Stokes-Thompson, Frederick , Cox, Robert , Crowther, Patricia , Atkins, Clare , McDonald, Marcus , Reiners, Torsten , Wood, Lincoln , Sim, Jenny , Grant, Scott , Campbell, Chris , Hillier, Mathew , Meredith, Grant , Steel, Caroline , Jegathesan, Jay Jay , Zagami, Jason , Sukunesan, Suku , Gaukrodger, Belma , Schutt, Stefan , Le Rossignol, Karen , Hill, Matthew , Rive, Pete , Xiangyu, Wang
Date: 2014
Type: Text , Conference paper
Relation: Proceedings of ASCILITE 2014 - Annual Conference of the Australian Society for Computers in Tertiary Education p. 279-289
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Description: The emergence of any new educational technology is often accompanied by inflated expectations about its potential for transforming pedagogical practice and improving student learning outcomes. A critique of the rhetoric accompanying the evolution of 3D virtual world education reveals a similar pattern, with the initial hype based more on rhetoric than research demonstrating the extent to which rhetoric matches reality. Addressed are the perceived gaps in the literature through a critique of the rhetoric evident throughout the evolution of the application of virtual worlds in education and the reality based on the reported experiences of experts in the field of educational technology, who are all members of the Australian and New Zealand Virtual Worlds Working Group. The experiences reported highlight a range of effective virtual world collaborative and communicative teaching experiences conducted in members' institutions. Perspectives vary from those whose reality is the actuation of the initial rhetoric in the early years of virtual world education, to those whose reality is fraught with challenges that belie the rhetoric. Although there are concerns over institutional resistance, restrictions, and outdated processes on the one-hand, and excitement over the rapid emergence of innovation on the other, the prevailing reality seems to be that virtual world education is both persistent and sustainable. Explored are critical perspectives on the rhetoric and reality on the educational uptake and use of virtual worlds in higher education, providing an overview of the current and future directions for learning in virtual worlds.

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