Scholarlink

<p>In the era of global health crises, social media has become both a mirror and amplifier of public opinion, influencing individual behaviours, policy responses, and the spread of (mis)information. Traditional monitoring techniques—such as surveys and focus groups—lack the timeliness, scalability, and granularity required for fast-moving health emergencies. This study presents a hybrid data mining framework that integrates sentiment analysis, topic modelling, and geolocation analytics to deliver a multidimensional view of pandemic-related public discourse. Using approximately 57,000 COVID-19-related tweets extracted via the Twitter API, lexicon-based sentiment analysis tools (VADER and TextBlob), Latent Dirichlet Allocation (LDA) topic modeling, and Orange Data Mining’s Document Map geolocation feature to capture public sentiment, thematic structures, and geographic patterns are employed. Results show a predominance of neutral sentiment (52.4%), with major topics including public health measures, vaccination discourse, and misinformation narratives. The COVID-19 pandemic has underscored the critical role of social media in shaping public discourse, disseminating information, and influencing public health decision-making. This study presents a hybrid data mining framework that integrates sentiment analysis, topic modeling, and geolocation analytics to provide a multidimensional understanding of pandemic-related discussions. Geolocation mapping revealed regional variations in sentiment, particularly higher vaccine skepticism in certain countries. The integrated framework demonstrates a reproducible, user-friendly, and region-aware methodology for crisis informatics, offering actionable insights for policymakers and public health agencies. The framework aligns with WHO infodemic management guidance and recent ethics recommendations, offering a practical, governance-ready model for health ministries and research institutions in low- and middle-income countries (LMICs) (WHO, in Social listening in infodemic management for public health: ethical guidance, World Health Organization, Geneva, 2025; Bhatt et al. in Public Health Rev 46:11. 10.3389/phrs.2025.00011, 2025 and Cascella et al. in Humanit Soc Sci Commun 12:76. 10.1057/s41599-025-04564-x, 2025).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Hybrid data mining for pandemic public opinion analysis: integrating sentiment, topic, and geolocation data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月22日

查看全文
  • Festus A. Omojowo

摘要

In the era of global health crises, social media has become both a mirror and amplifier of public opinion, influencing individual behaviours, policy responses, and the spread of (mis)information. Traditional monitoring techniques—such as surveys and focus groups—lack the timeliness, scalability, and granularity required for fast-moving health emergencies. This study presents a hybrid data mining framework that integrates sentiment analysis, topic modelling, and geolocation analytics to deliver a multidimensional view of pandemic-related public discourse. Using approximately 57,000 COVID-19-related tweets extracted via the Twitter API, lexicon-based sentiment analysis tools (VADER and TextBlob), Latent Dirichlet Allocation (LDA) topic modeling, and Orange Data Mining’s Document Map geolocation feature to capture public sentiment, thematic structures, and geographic patterns are employed. Results show a predominance of neutral sentiment (52.4%), with major topics including public health measures, vaccination discourse, and misinformation narratives. The COVID-19 pandemic has underscored the critical role of social media in shaping public discourse, disseminating information, and influencing public health decision-making. This study presents a hybrid data mining framework that integrates sentiment analysis, topic modeling, and geolocation analytics to provide a multidimensional understanding of pandemic-related discussions. Geolocation mapping revealed regional variations in sentiment, particularly higher vaccine skepticism in certain countries. The integrated framework demonstrates a reproducible, user-friendly, and region-aware methodology for crisis informatics, offering actionable insights for policymakers and public health agencies. The framework aligns with WHO infodemic management guidance and recent ethics recommendations, offering a practical, governance-ready model for health ministries and research institutions in low- and middle-income countries (LMICs) (WHO, in Social listening in infodemic management for public health: ethical guidance, World Health Organization, Geneva, 2025; Bhatt et al. in Public Health Rev 46:11. 10.3389/phrs.2025.00011, 2025 and Cascella et al. in Humanit Soc Sci Commun 12:76. 10.1057/s41599-025-04564-x, 2025).