Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights

TL;DR

This study compares public sentiment towards COVID-19 and Monkeypox using machine learning on large Twitter datasets, revealing key differences in emotions, discourse, and implications for public health communication.

Contribution

It introduces a comprehensive comparative sentiment analysis framework applied to two major health crises, utilizing advanced NLP models and large-scale social media data.

Findings

Significant differences in sentiment polarity between COVID-19 and Monkeypox.

Media influence and disease characteristics shape public discourse.

Insights aid in designing targeted health communication strategies.

Abstract

The emergence of global health crises, such as COVID-19 and Monkeypox (mpox), has underscored the importance of understanding public sentiment to inform effective public health strategies. This study conducts a comparative sentiment analysis of public perceptions surrounding COVID-19 and mpox by leveraging extensive datasets of 147,475 and 106,638 tweets, respectively. Advanced machine learning models, including Logistic Regression, Naive Bayes, RoBERTa, DistilRoBERTa and XLNet, were applied to perform sentiment classification, with results indicating key trends in public emotion and discourse. The analysis highlights significant differences in public sentiment driven by disease characteristics, media representation, and pandemic fatigue. Through the lens of sentiment polarity and thematic trends, this study offers valuable insights into tailoring public health messaging, mitigating misinformation, and fostering trust during concurrent health crises. The findings contribute to advancing sentiment analysis applications in public health informatics, setting the groundwork for enhanced real-time monitoring and multilingual analysis in future research.