arXiv:2507.16820cs.SIcs.AI2025-07

分析疫情后灾害信息学研究趋势,揭示全球合作与公共卫生重点转移。

Disaster Informatics after the COVID-19 Pandemic: Bibliometric and Topic Analysis based on Large-scale Academic Literature

  • 用大语言模型和生成AI分析2020-2022年海量文献
  • 疫情重灾区国家研究活跃度高,聚焦公共卫生与韧性策略
  • 适合政策制定者、学者及灾害应对实践者参考

本研究对2020年1月至2022年9月期间发表的灾害信息学文献进行了全面的计量分析与主题挖掘。基于大规模语料库及预训练语言模型、生成式AI等先进技术,识别出最活跃的国家、机构、作者、合作网络、新兴主题、主要主题演变模式以及新冠疫情引发的研究优先级变化。研究发现:(1)受疫情影响严重的国家在研究上也最为活跃,且各有侧重;(2)同一地区或使用同一种语言的国家与机构更易形成合作;(3)顶尖作者通常与一两位关键伙伴保持紧密协作;(4)作者多专注1-2个主题,而机构则跨多个主题布局;(5)新冠疫情推动研究重心向公共卫生倾斜。此外,该领域正趋向于多维度韧性策略与跨部门数据共享合作,反映出对全球脆弱性与相互依赖性的更高认知。数据收集与质控、数据分析实践、基于LLM的主题提取与摘要方法、结果可视化工具可推广至类似数据集或分析问题。通过绘制灾害信息学发展趋势图谱,本研究为政策制定者、实践者与学者提升灾害应对能力提供战略参考。

原文摘要 · Abstract (English)

This study presents a comprehensive bibliometric and topic analysis of the disaster informatics literature published between January 2020 to September 2022. Leveraging a large-scale corpus and advanced techniques such as pre-trained language models and generative AI, we identify the most active countries, institutions, authors, collaboration networks, emergent topics, patterns among the most significant topics, and shifts in research priorities spurred by the COVID-19 pandemic. Our findings highlight (1) countries that were most impacted by the COVID-19 pandemic were also among the most active, with each country having specific research interests, (2) countries and institutions within the same region or share a common language tend to collaborate, (3) top active authors tend to form close partnerships with one or two key partners, (4) authors typically specialized in one or two specific topics, while institutions had more diverse interests across several topics, and (5) the COVID-19 pandemic has influenced research priorities in disaster informatics, placing greater emphasis on public health. We further demonstrate that the field is converging on multidimensional resilience strategies and cross-sectoral data-sharing collaborations or projects, reflecting a heightened awareness of global vulnerability and interdependency. Collecting and quality assurance strategies, data analytic practices, LLM-based topic extraction and summarization approaches, and result visualization tools can be applied to comparable datasets or solve similar analytic problems. By mapping out the trends in disaster informatics, our analysis offers strategic insights for policymakers, practitioners, and scholars aiming to enhance disaster informatics capacities in an increasingly uncertain and complex risk landscape.

灾害信息学文献计量疫情影响主题分析

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