Tracking sentiments toward fat acceptance over a decade on Twitter

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

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.

Exploring the weight bias of professionals working in the field of obesity with a mobile IAT: a pilot study

BACKGROUND

Obesity is common in many industrialized nations and often accompanied by related health issues. Furthermore, individuals living with overweight or obesity are often confronted with stigmatization in their daily lives. These problems may be aggravated if the objectivity of health care professionals is compromised due to (unconscious) prejudices. If pharmaceutical companies, regulatory agencies, and health insurers are also susceptible to these biases, decisions related to the development, approval, and reimbursement of obesity-related therapies may be negatively impacted.

MATERIALS AND METHODS

The 'Implicit Association Test' (IAT) is a psychometric test allowing to measure these attitudes and could therefore assist to reveal unconscious preferences. A self-developed mobile version, in the form of a ResearchKit-based IAT app was employed in the presented study. The objective was to determine (potential) weight bias and its characteristics for professionals attending a national obesity-related conference in Germany (G1), compared to a control group (without stated interest in the topic, G2) - both using the mobile app - and a historical control (G3) based on data provided by Project Implicit acquired by a web app.

RESULTS

Explicit evaluations of G1 were neutral at a higher percentage compared with G2 and G3, while implicit preference toward lean individuals did not differ significantly between G2 and G3, and G1.

CONCLUSION

The greater discrepancy between the (more neutral) explicit attitude and the unconscious preference pointing in the anti-obesity direction could indicate an underestimated bias for the professional participants in G1. Implicit preference is often ingrained from childhood on, and difficult to overcome. Thus, even for professionals, it may unconsciously influence decisions made in the care they provide. Professionals in any given health care sector directed at obesity care should thus be made aware of this inconsistency to enable them to consciously counteract this potential effect.