Disaster Informatics after the COVID-19 Pandemic: Bibliometric and Topic Analysis based on Large-scale Academic Literature
- URL: http://arxiv.org/abs/2507.16820v1
- Date: Sat, 28 Jun 2025 20:30:36 GMT
- Title: Disaster Informatics after the COVID-19 Pandemic: Bibliometric and Topic Analysis based on Large-scale Academic Literature
- Authors: Ngan Tran, Haihua Chen, Ana Cleveland, Yuhan Zhou,
- Abstract summary: This study presents a comprehensive bibliometric and topic analysis of the disaster informatics literature published between January 2020 to September 2022.<n>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.
- Score: 0.7340017786387768
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Modular versus Hierarchical: A Structural Signature of Topic Popularity in Mathematical Research [0.0]
We study how the popularity of a research topic is associated with the structure of that topic's collaboration network.<n>Our findings suggest that topic selection is an implicit choice between two fundamentally different collaborative environments.
arXiv Detail & Related papers (2025-06-28T16:39:57Z) - CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers [3.929864777332447]
CS-PaperSum is a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences.<n>Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery.
arXiv Detail & Related papers (2025-02-27T22:48:35Z) - Intervention strategies for misinformation sharing on social media: A bibliometric analysis [1.8020166013859684]
Inaccurate shared information causes confusion, can adversely affect mental health, and can lead to mis-informed decision-making.
This study explores the typology of intervention strategies for addressing misinformation sharing on social media.
It identifies 4 important clusters - cognition-based, automated-based, information-based, and hybrid-based.
arXiv Detail & Related papers (2024-09-26T08:38:15Z) - Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports.
We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes.
Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
arXiv Detail & Related papers (2024-06-16T03:10:16Z) - A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [55.33653554387953]
Pattern Analysis and Machine Intelligence (PAMI) has led to numerous literature reviews aimed at collecting and fragmented information.<n>This paper presents a thorough analysis of these literature reviews within the PAMI field.<n>We try to address three core research questions: (1) What are the prevalent structural and statistical characteristics of PAMI literature reviews; (2) What strategies can researchers employ to efficiently navigate the growing corpus of reviews; and (3) What are the advantages and limitations of AI-generated reviews compared to human-authored ones.
arXiv Detail & Related papers (2024-02-20T11:28:50Z) - AI in Supply Chain Risk Assessment: A Systematic Literature Review and Bibliometric Analysis [0.0]
This study examines 1,903 articles from Google Scholar and Web of Science, with 54 studies selected through PRISMA guidelines.<n>Our findings reveal that ML models, including Random Forest, XGBoost, and hybrid approaches, significantly enhance risk prediction accuracy and adaptability in post-pandemic contexts.<n>The study underscores the necessity of dynamic strategies, interdisciplinary collaboration, and continuous model evaluation to address challenges such as data quality and interpretability.
arXiv Detail & Related papers (2023-12-12T17:47:51Z) - Responsible AI Considerations in Text Summarization Research: A Review
of Current Practices [89.85174013619883]
We focus on text summarization, a common NLP task largely overlooked by the responsible AI community.
We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022.
We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals.
arXiv Detail & Related papers (2023-11-18T15:35:36Z) - A Comprehensive Study of Groundbreaking Machine Learning Research:
Analyzing highly cited and impactful publications across six decades [1.6442870218029522]
Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields.
It is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far.
arXiv Detail & Related papers (2023-08-01T21:43:22Z) - Understanding the temporal evolution of COVID-19 research through
machine learning and natural language processing [66.63200823918429]
The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world.
We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research.
Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
arXiv Detail & Related papers (2020-07-22T18:02:39Z) - When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and
Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures.
Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential.
This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
arXiv Detail & Related papers (2020-05-13T18:21:50Z) - Fighting the COVID-19 Infodemic: Modeling the Perspective of
Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the
Society [37.9389191670008]
COVID-19 has been declared one of the most important focus areas of the World Health Organization.
Fighting this infodemic has been declared one of the most important focus areas of the World Health Organization.
We release a large dataset of 16K manually annotated tweets for fine-grained disinformation analysis.
arXiv Detail & Related papers (2020-04-30T18:04:20Z) - Mapping the Landscape of Artificial Intelligence Applications against
COVID-19 [59.30734371401316]
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization.
We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
arXiv Detail & Related papers (2020-03-25T12:30:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.