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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Aug 24, 2023
Open Peer Review Period: Aug 24, 2023 - Oct 19, 2023
Date Accepted: Jun 27, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study

Correia JC, Ahmad SS, Waqas A, Meraj H, Pataky Z

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study

J Med Internet Res 2024;26:e52142

DOI: 10.2196/52142

PMID: 39393064

PMCID: 11512131

Obesity during the COVID-19 pandemic: Unraveling Public Emotions through Sentiment Analysis & Topic Modeling

  • Jorge Cesar Correia; 
  • Sarmad Shaharyar Ahmad; 
  • Ahmed Waqas; 
  • Hafsa Meraj; 
  • Zoltan Pataky

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 towards obesity is essential for developing effective health policies, prevention strategies, and treatment approaches.

Objective:

This study investigates the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter.

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 negative comments on President Trump's obesity struggle and Boris Johnson's criticized obesity campaign. Ben Shapiro's remarks on not vaccinating people with obesity for COVID-19 also sparked outrage. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity, President Trump's obesity struggle, COVID-19 vaccinations, Boris Johnson's obesity campaign, body shaming, racism and high obesity rates among Black Americans, 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.


 Citation

Please cite as:

Correia JC, Ahmad SS, Waqas A, Meraj H, Pataky Z

Exploring Public Emotions on Obesity During the COVID-19 Pandemic Using Sentiment Analysis and Topic Modeling: Cross-Sectional Study

J Med Internet Res 2024;26:e52142

DOI: 10.2196/52142

PMID: 39393064

PMCID: 11512131

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.