Navigating the asthma network on Twitter: Insights from social network and sentiment analysis

Pratiwi, Hening; Benkő, Ria; Kusuma, Ikhwan Yuda

doi:10.1177/20552076231224075

Cited by 4 publications

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

Correia,

Ahmad,

Waqas

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

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.

show abstract

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

Correia,

Ahmad,

Waqas

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping

Jaiswal,

Shah,

Harjadi

et al. 2024

JMIR Form Res

View full text Add to dashboard Cite

Digital phenotyping, or personal sensing, is a field of research that seeks to quantify traits and characteristics of people using digital technologies, usually for health care purposes. In this commentary, we discuss emerging ethical issues regarding the use of social media as training data for artificial intelligence (AI) models used for digital phenotyping. In particular, we describe the ethical need for explicit consent from social media users, particularly in cases where sensitive information such as labels related to neurodiversity are scraped. We also advocate for the use of community-based participatory design principles when developing health care AI models using social media data.

show abstract

Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping (Preprint)

Jaiswal,

Shah,

Harjadi

et al. 2024

Preprint

View full text Add to dashboard Cite

UNSTRUCTURED Digital phenotyping, or personal sensing, is a field of research that seeks to quantify traits and characteristics of people using digital technologies, usually for health care purposes. In this commentary, we discuss emerging ethical issues regarding the use of social media as training data for artificial intelligence (AI) models used for digital phenotyping. In particular, we describe the ethical need for explicit consent from social media users, particularly in cases where sensitive information such as labels related to neurodiversity are scraped. We also advocate for the use of community-based participatory design principles when developing health care AI models using social media data.

show abstract

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis

Cited by 4 publications

References 56 publications

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

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

Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping

Ethics of the Use of Social Media as Training Data for AI Models Used for Digital Phenotyping (Preprint)

Contact Info

Product

Resources

About