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

被引:0
|
作者
Correia, Jorge Cesar [1 ,2 ]
Ahmad, Sarmad Shaharyar [3 ]
Waqas, Ahmed [4 ]
Meraj, Hafsa [5 ]
Pataky, Zoltan [1 ,2 ]
机构
[1] Univ Hosp Geneva, WHO Collaborating Ctr, Unit Therapeut Patient Educ, Chemin Venel 7, CH-1206 Geneva, Switzerland
[2] Univ Geneva, Chemin Venel 7, CH-1206 Geneva, Switzerland
[3] Liverpool Hope Univ, Sch Math Comp Sci & Engn, Liverpool, England
[4] Univ Liverpool, Inst Populat Hlth, Dept Primary Care & Mental Hlth, Liverpool, England
[5] Greater Manchester Mental Hlth NHS Fdn Trust, Salford, England
关键词
obesity; Twitter; infodemic; attitude; opinion; perception; perspective; obese; weight; overweight; social media; tweet; sentiment; topic modeling; BERT; Bidirectional Encoder Representations from Transformers; NLP; natural language processing; generalpublic; celebrities; WEIGHT STIGMA; OUTCOMES; IMPACT;
D O I
10.2196/52142
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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 theUnited 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
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
    Boon-Itt, Sakun
    Skunkan, Yukolpat
    JMIR PUBLIC HEALTH AND SURVEILLANCE, 2020, 6 (04): : 245 - 261
  • [2] Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter
    Xue, Jia
    Chen, Junxiang
    Chen, Chen
    Zheng, Chengda
    Li, Sijia
    Zhu, Tingshao
    PLOS ONE, 2020, 15 (09):
  • [3] The public image of nursing during the COVID-19 pandemic: A cross-sectional study
    Yavas, Gamze
    Ozerli, Ayla Nur
    INTERNATIONAL NURSING REVIEW, 2025, 72 (01)
  • [4] Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis
    Xie R.
    Chu S.K.W.
    Chiu D.K.W.
    Wang Y.
    Data and Information Management, 2021, 5 (01) : 86 - 99
  • [5] Exploring Public Sentiment During COVID-19: A Cross Country Analysis
    Yu, Shuo
    He, Sihan
    Cai, Zhen
    Lee, Ivan
    Naseriparsa, Mehdi
    Xia, Feng
    IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2023, 10 (03) : 1083 - 1094
  • [6] Topic modelling and sentiment analysis during COVID-19 revealed emotions changes for public health
    Figueiredo, S.
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [7] Exploring the public's perception of gambling addiction on Twitter during the COVID-19 pandemic: Topic modelling and sentiment analysis
    Fino, Emanuele
    Hanna-Khalil, Bishoy
    Griffiths, Mark D.
    JOURNAL OF ADDICTIVE DISEASES, 2021, 39 (04) : 489 - 503
  • [8] Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach
    Alshanik, Farah
    Khasawneh, Rawand
    Dalky, Alaa
    Qawasmeh, Ethar
    JMIR INFODEMIOLOGY, 2025, 5
  • [9] User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis
    Chin, Hyojin
    Lima, Gabriel
    Shin, Mingi
    Zhunis, Assem
    Cha, Chiyoung
    Choi, Junghoi
    Cha, Meeyoung
    JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25
  • [10] Analysis of tweets on toothache during the COVID-19 pandemic using the CrystalFeel algorithm: a cross-sectional study
    Halenur Altan
    Alem Coşgun
    BMC Oral Health, 21