Classification of Twitter users with eating disorder engagement: Learning from the biographies. (March 2023)

• Record Type: Journal Article
• Authors: Abuhassan, Mohammad; Anwar, Tarique; Fuller-Tyszkiewicz, Matthew; Jarman, Hannah K.; Shatte, Adrian; Liu, Chengfei; Sukunesan, Suku
• Is Part Of: Computers in human behavior. Volume 140(2023)
• Publication Date: 2023-03
• Subjects: Eating disorders -- Mental health -- Text classification -- Deep learning -- Social media; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2022.107519
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: 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 F 1 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. Highlights: Twitter biographies of 1.4 million users with ED engagement are collected. A subset of the biographies is manually annotated with user categories. The proposed model achieves an F1 score of 98.19% and an accuracy of 98.37%. Our network analysis suggests that ED-users maintain restricted interactions. Healthcare professionals and communicators play an essential role in addressing EDs.