Weight-Biased Language across 30 Years of Australian News Reporting on Obesity: Associations with Public Health Policy

Abstract

(1) Background: This study tracked the reporting of obesity in the Australian news media over three decades and how changing representations over time were linked to obesity-related public health policy developments. (2) Methods: Machine learning and computational language analysis techniques (word embedding, dichotomous bias mapping) were used to identify language biases associated with obesity in 157,237 relevant articles drawn from the Australian Dow Jones digital database of print news media articles from 1990 to 2019. (3) Results: Obesity-related terms were stigmatised on four key dimensions (gender, health, socioeconomic status, stereotypes), with language biased towards femininity and lower socioeconomic status in particular. Biases remained relatively steady from 2005 to 2019, despite recent policy initiatives directly seeking to address obesity stigma. To some degree, for each of the four dimensions, cosine values moved toward 0 over time (i.e., no association with one dimension poll or the other), but remained around 0.20. There was a strong relationship between news media and public health policy discourse over the 30-year study period. (4) Conclusions: With increasing recognition of the health consequences of weight stigma, policymakers and the media must work together to ensure public weight management narratives avoid discourse that may stigmatise heavier individuals, particularly women, and/or reinforce negative obesity stereotypes.

Keywords

• obesity
• weight stigma
• media analysis
• language bias
• natural language processing