Scientific article
OA Policy
English

Exploring public emotions on obesity during the COVID-19 pandemic using sentiment analysis and topic modeling : cross-sectional study

Published inJMIR. Journal of medical internet research, vol. 26, e52142
Publication date2024-10-11
First online date2024-10-11
Abstract

Background

Obesity is a chronic, multifactorial, and relapsing disease, affecting people of all ages worldwide, and is directly related to multiple complications. Understanding public attitudes and perceptions toward obesity is essential for developing effective health policies, prevention strategies, and treatment approaches.

Objective

This study investigated the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter (subsequently rebranded as X).

Methods

The study analyzes a dataset of 53,414 tweets related to obesity posted on Twitter during the COVID-19 pandemic, from April 2019 to December 2022. Sentiment analysis was performed using the XLM-RoBERTa-base model, and topic modeling was conducted using the BERTopic library.

Results

The analysis revealed that tweets regarding obesity were predominantly negative. Spikes in Twitter activity correlated with significant political events, such as the exchange of obesity-related comments between US politicians and criticism of the United Kingdom’s obesity campaign. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity; the US President’s obesity struggle; COVID-19 vaccinations; the UK government’s obesity campaign; body shaming; racism and high obesity rates among Black American people; smoking, substance abuse, and alcohol consumption among people with obesity; environmental risk factors; and surgical treatments.

Conclusions

Twitter serves as a valuable source for understanding obesity-related sentiments and attitudes among the public, celebrities, and influential organizations. Sentiments regarding obesity were predominantly negative. Negative portrayals of obesity by influential politicians and celebrities were shown to contribute to negative public sentiments, which can have adverse effects on public health. It is essential for public figures to be mindful of their impact on public opinion and the potential consequences of their statements.

Keywords
  • BERT
  • Bidirectional Encoder Representations from Transformers
  • NLP
  • Twitter
  • Attitude
  • Celebrities
  • General public
  • Infodemic
  • Natural language processing
  • Obese
  • Obesity
  • Opinion
  • Overweight
  • Perception
  • Perspective
  • Sentiment
  • Social media
  • Topic modeling
  • Tweet
  • Weight
Citation (ISO format)
CORREIA, Jorge et al. Exploring public emotions on obesity during the COVID-19 pandemic using sentiment analysis and topic modeling : cross-sectional study. In: JMIR. Journal of medical internet research, 2024, vol. 26, p. e52142. doi: 10.2196/52142
Main files (1)
Article (Published version)
Secondary files (4)
Supplemental data
accessLevelPublic
Supplemental data
accessLevelPublic
Supplemental data
accessLevelPublic
Show more
Identifiers
Additional URL for this publicationhttps://www.jmir.org/2024/1/e52142
Journal ISSN1438-8871
51views
114downloads

Technical informations

Creation13/10/2024 10:25:54
First validation30/10/2024 13:47:08
Update time25/08/2025 11:38:29
Status update25/08/2025 11:38:29
Last indexation25/08/2025 11:39:14
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack