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

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Medical Internet Research Pub Date : 2024-10-11 DOI:10.2196/52142
Jorge César Correia, Sarmad Shaharyar Ahmad, Ahmed Waqas, Hafsa Meraj, Zoltan Pataky
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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.

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利用情感分析和主题建模探讨 COVID-19 大流行期间公众对肥胖的情绪:横断面研究。
背景:肥胖症是一种慢性、多因素和复发性疾病,影响着全世界各个年龄段的人群,并与多种并发症直接相关。了解公众对肥胖症的态度和看法对于制定有效的健康政策、预防策略和治疗方法至关重要:本研究利用社交媒体数据,特别是 Twitter(后更名为 X)的数据,调查了公众、名人和重要组织对肥胖的看法:本研究分析了 COVID-19 大流行期间(2019 年 4 月至 2022 年 12 月)在 Twitter 上发布的 53,414 条与肥胖相关的推文数据集。使用 XLM-RoBERTa-base 模型进行了情感分析,并使用 BERTopic 库进行了话题建模:分析结果显示,有关肥胖的推文主要是负面的。推特活动的峰值与重大政治事件相关,如美国政治家之间有关肥胖的评论交流和对英国肥胖运动的批评。主题建模确定了 243 个代表各种肥胖相关主题的集群,如儿童肥胖症;美国总统的肥胖症斗争;COVID-19 疫苗接种;英国政府的肥胖症运动;身体羞辱;种族主义和美国黑人的高肥胖率;肥胖症患者的吸烟、药物滥用和酗酒;环境风险因素;以及手术治疗:推特是了解公众、名人和有影响力的组织中与肥胖相关的情绪和态度的重要来源。有关肥胖的情绪主要是负面的。有影响力的政治家和名人对肥胖的负面描述被证明会助长公众的负面情绪,从而对公众健康产生不利影响。公众人物必须意识到他们对公众舆论的影响及其言论的潜在后果。
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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