Bibliometric Mapping of Flash Flood and Emergency Response Research: The Position of Public Response
Main Article Content
Abstract
Public response to emergency measures is critical to the effectiveness of flash flood disaster management because these events occur suddenly and provide only a limited warning period. Although research on disaster emergency response has expanded, bibliometric evidence concerning the position of public response within the scientific literature on flash floods remains limited. This study maps the thematic structure and relative position of public response in flash flood and emergency response research. A quantitative bibliometric approach was employed using 991 documents retrieved from Google Scholar through a Boolean search for “flash flood,” “public response,” and “emergency response” in article titles, abstracts, and keywords. The documents were exported in CSV format and analyzed using VOSviewer 1.6.21 through network and density visualizations. The findings indicate that “flash flood” was closely connected to the core themes of emergency response, including disaster, case study, emergency service, disaster management, policy, landslide, and disaster risk reduction, with 63 occurrences, 141 links, and a total link strength of 395. By contrast, “public response” occupied a peripheral position and displayed substantially weaker connections within the network. Density visualization further showed that the most concentrated research areas surrounded the terms “disaster” and “policy,” whereas “flash flood” was positioned at the boundary of this high-density area. The separate clustering of “flash flood” and “flash flooding” also indicates terminological inconsistency across the analyzed literature. These findings demonstrate that flash flood research remains dominated by operational, institutional, and policy-oriented dimensions of emergency response, while public responses to emergency directives remain underexplored. This study contributes to the identification of thematic gaps and highlights the need for standardized terminology and further research on how communities interpret and respond to emergency instructions during flash flood events.
Downloads
Citation Metrics & Similar Scopus Articles
-
Chano J. (2027)Lesson Study and School as a Learning Community to Support Sustainable Development Goals (SDGs): Definition, Literature Review, and Bibliometric MappingAsean Journal of Educational Research and Technology, 6(1), 153-170
-
Otsuka M. (2027)Effectiveness of Simulation-based Training on Emergency Response Knowledge Among Inter-Professional Staff Involved in Gastrointestinal Endoscopic PracticeDen Open, 7(1)
-
Wang T. (2027)Experimental comparison of dual-stage organic Rankine cycle and organic Rankine flash cycle for low-temperature geothermal power generationUnconventional Resources, 17
Article Details

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
Ahlgren, O. (2017). Research on sentiment analysis: The first decade. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (pp. 890–899). IEEE. https://doi.org/10.1109/ICDMW.2016.0131
Anstead, N., & O’Loughlin, B. (2015). Social media analysis and public opinion: The 2010 UK general election. Journal of Computer-Mediated Communication, 20(2), 204–220. https://doi.org/10.1111/jcc4.12102
Georgiadou, E., Angelopoulos, S., & Drake, H. (2020). Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes. International Journal of Information Management, 51, Article 102048. https://doi.org/10.1016/j.ijinfomgt.2019.102048
Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., Turner McGowen, S. E., Davis, R., Vaid, S., McElderry, J. A., & Griffith, S. A. (2015). Social media and disasters: A functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1–22. https://doi.org/10.1111/disa.12092
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys, 47(4), Article 67. https://doi.org/10.1145/2771588
Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., & Cebrian, M. (2016). Rapid assessment of disaster damage using social media activity. Science Advances, 2(3), Article e1500779. https://doi.org/10.1126/sciadv.1500779
Mejia, C., Wu, M., Zhang, Y., & Kajikawa, Y. (2021). Exploring topics in bibliometric research through citation networks and semantic analysis. Frontiers in Research Metrics and Analytics, 6, Article 742311. https://doi.org/10.3389/frma.2021.742311
Paul, J., & Barari, M. (2022). Meta-analysis and traditional systematic literature reviews—What, why, when, where, and how? Psychology & Marketing, 39(6), 1099–1115. https://doi.org/10.1002/mar.21657
Permana, I., & Maani, K. D. (2024). Publication trend of public sentiment towards Indonesia government policies. Sinkron: Jurnal dan Penelitian Teknik Informatika, 8(3), 2061–2069. https://doi.org/10.33395/sinkron.v8i3.13843
Rasul, T., Lim, W. M., Dowling, M., Kumar, S., & Rather, R. A. (2022). Advertising expenditure and stock performance: A bibliometric analysis. Finance Research Letters, 50, Article 103283. https://doi.org/10.1016/j.frl.2022.103283
Sarirete, A. (2021). A bibliometric analysis of COVID-19 vaccines and sentiment analysis. Procedia Computer Science, 194, 280–287. https://doi.org/10.1016/j.procs.2021.10.083
Su, M., Peng, H., & Li, S. (2021). A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE). Expert Systems with Applications, 186, Article 115728. https://doi.org/10.1016/j.eswa.2021.115728
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Vieweg, S., Hughes, A. L., Starbird, K., & Palen, L. (2010). Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1079–1088). Association for Computing Machinery. https://doi.org/10.1145/1753326.1753486
Wang, Z., Chong, C. S., Lan, L., Yang, Y., Ho, S. B., & Tong, J. C. (2016). Fine-grained sentiment analysis of social media with emotion sensing. In 2016 Future Technologies Conference (FTC) (pp. 1361–1364). IEEE. https://doi.org/10.1109/FTC.2016.7821783
Xie, T., Wei, Y., Chen, W., & Huang, H. (2020). Parallel evolution and response decision method for public sentiment based on system dynamics. European Journal of Operational Research, 287(3), 1131–1148. https://doi.org/10.1016/j.ejor.2020.05.025
Yang, L., Marmolejo-Duarte, C., & Martí-Ciriquián, P. (2022). Quantifying the relationship between public sentiment and urban environment in Barcelona. Cities, 130, Article 103977. https://doi.org/10.1016/j.cities.2022.103977






















