Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review

Abstract

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.

Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis

“…Methods associated with fat stigma studies on social media discourses were systematically reviewed by Wanniarachchi et al (2022). The review ascertained that textual data analysis in most studies was done mainly using manual qualitative coding approaches, although, few studies have used sentiment analysis, topic modelling and emotion analysis too.…”

“…Discourse analysis can enable social scientists to recognize emergent stigma patterns present in the given social context. Moreover, prior studies have mostly considered Twitter as their single data source (Wanniarachchi et al, 2022); but, with the text-limit constraints imposed on tweets, users have to adapt their intended message within the specified data bounds. We propose that by combining data from multiple social media platforms, we can inspect richer content.…”

“…Empirical data that is contextdependent on the selected keywords are subsequently scraped for further analysis. Fat studies have made significant use of Twitter in comparison to other social media platforms (Wanniarachchi et al, 2022); however, other platforms, such as Reddit (which appears "more as a discussion forum, and [its] comments have a wider range of document lengths than Twitter data" (Curiskis et al, 2020)) too are gathering momentum. Twitter and Reddit platforms served as our primary data sources.…”

“…Australasian Journal of Information Systems Wanniarachchi et al 2023, Vol 27, Research Article Hate Speech Pattern in Social Media sentiment analysis on the observed texts is rather straightforward (Curiskis et al, 2020;Wanniarachchi et al, 2022).…”

“…Hate speech expressions over social media, specifically hate content targeting excessive weight (or fatness) have been much studied (i.e., Brooker et al, 2018;Chou, Prestin, & Kunath, 2014;Holmberg et al, 2018;Hussin, Frazier, & Thompson, 2011;Jeon et al, 2018;Kent et al, 2016;Lydecker et al, 2016); however, very few of these have used natural language processing (NLP) techniques (Wanniarachchi et al, 2022). Prior studies are found to be limited to manual analysis techniques that make use of small text-based datasets acquired from social media, but recent advances in machine learning allow for large datasets to be effectively analysed computationally.…”