对比新冠与猴痘的公众情绪，揭示防疫传播差异
Comparative sentiment analysis of public perception: Monkeypox vs. COVID-19 behavioral insights
- 用14万+和10万+推文，结合多种机器学习模型分析公众情绪
- 发现公众对两种疾病的情绪倾向、关注重点存在显著差异
- 适合公共卫生决策者与舆情研究者参考
新冠疫情与猴痘（mpox）等全球健康危机的出现，凸显了理解公众情绪对制定有效公共卫生策略的重要性。本研究基于147,475条新冠推文和106,638条猴痘推文，采用逻辑回归、朴素贝叶斯、RoBERTa、DistilRoBERTa及XLNet等先进机器学习模型，开展比较情感分析。结果揭示了疾病特征、媒体报道与疫情疲劳等因素导致的公众情绪与话语模式差异。通过情感极性与主题趋势的分析，为优化公共卫生信息传播、减少错误信息、增强公众信任提供了洞见。研究推动了情感分析在公共卫生信息学中的应用，为未来实时监测与多语言分析奠定了基础。
原文摘要 · Abstract (English)
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.
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