Mining and Analyzing the Evolution of Public Opinion in Extreme Disaster Events from Social Media: A Case Study of the 2022 Yingde Flood in China
Abstract
Natural disasters have caused significant economic losses and casualties. Obtaining detailed disaster information and understanding public opinion during disasters are crucial for devising effective policies and ensuring timely disaster responses. With the widespread use of social media, it has become an important channel for extracting disaster information. However, accurately obtaining and revealing public opinion from social media remains challenging. This study combines the biterm topic model and support vector machine to analyze topic features. Additionally, sentiment features are analyzed using the Generative Pre-trained Transformer-3.5 model. These techniques are employed to build a social media-based flood information mining model capable of detecting the spatiotemporal distribution of public sentiment and discussion topics, including significant events impacting public sentiment. Using the 2022 Yingde flood as a case study, we explored the evolutionary patterns of public opinion on floods across three dimensions: time, space, and content. The study also explored the correlation between flooding and public opinion through geographic visualization and statistical analysis. The results indicated a precision of 89.2% and 80.2% for topic and sentiment classification, respectively. Temporally, the public response to flooding was primarily concentrated during heavy rainfall and flooding, varying with disaster severity. Furthermore, significant events or statements by public figures can greatly influence public responses to flooding. Spatially, the public response to flooding focused mainly in major urban areas and severely affected regions. In terms of content, a strong correlation was revealed between sentiments, topic distribution, and the disaster scenario. The findings can be used to analyze disaster conditions and public opinion in depth, and as a supplement of existing methods of extracting disaster information, it can enhance situational awareness for disaster emergency management and provide a reference basis for emergency relief efforts.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (Grant No. 72361035), Practical Innovation Project of Postgraduate Students in the Professional Degree of Yunnan University (Grant No. ZC-23236094), Yunnan Fundamental Research Project (Grant No. 202401BF070001-026), Yunnan Province Postgraduate Joint Training Base Project of Integration of Industry and Education, Science and Technology Plan Project of Department of Housing and Urban-Rural Development of Yunnan Province (Grant No. K00000135), and Graduate Ideological and Political Demonstration Course Project of Yunnan University (Grant No. KCSZ202301), as well as the support from Guangzhou South Surveying and Mapping Technology Co., Ltd.
References
Ahmad, M., S. Aftab, and I. Ali. 2017. “Sentiment analysis of tweets using SVM.” Int. J. Comput. Appl. 177 (5): 25–29. https://doi.org/10.5120/ijca2017915758.
Alfarrarjeh, A., S. Agrawal, S. H. Kim, and C. Shahabi. 2017. “Geo-spatial multimedia sentiment analysis in disasters.” In Proc., IEEE Int. Conf. on Data Science and Advanced Analytics (DSAA), 193–202. New York: IEEE. https://doi.org/10.1109/DSAA.2017.77.
Alto, V. 2023. Modern generative AI with ChatGPT and OpenAI models: Leverage the capabilities of OpenAI’s LLM for productivity and innovation with GPT3 and GPT4. Birmingham, UK: Packt Publishing Ltd.
Belal, M., J. She, and S. Wong. 2023. “Leveraging ChatGPT as text annotation tool for sentiment analysis.” Preprint, submitted June 18, 2023. http://arxiv.org/abs/2306.17177.
Blanford, J. I., J. Bernhardt, A. Savelyev, G. Wong-Parodi, A. M. Carleton, D. W. Titley, and A. M. MacEachren. 2014. “Tweeting and tornadoes.” In Proc., ISCRAM, 319–323. University Park, PA: Pennsylvania State Univ.
Boitel, E., A. Mohasseb, and E. Haig. 2024. “A comparative analysis of GPT-3 and BERT models for text-based emotion recognition: Performance, efficiency, and robustness.” In Advances in computational intelligence systems, advances in intelligent systems and computing, edited by N. Naik, P. Jenkins, P. Grace, L. Yang, and S. Prajapat, 567–579. Cham, Switzerland: Springer.
Chan, F. K. S., J. A. Griffiths, D. Higgitt, S. Xu, F. Zhu, Y.-T. Tang, Y. Xu, and C. R. Thorne. 2018. “Sponge City’ in China—A breakthrough of planning and flood risk management in the urban context.” Land Use Policy 76 (Jul): 772–778. https://doi.org/10.1016/j.landusepol.2018.03.005.
Cheng, T., and T. Wicks. 2014. “Event detection using Twitter: A spatiotemporal approach.” PLoS One 9 (6): e97807. https://doi.org/10.1371/journal.pone.0097807.
Choi, S., and B. Bae. 2015. “The real-time monitoring system of social big data for disaster management.” In Computer science and its applications: Ubiquitous information technologies, 809–815. Berlin: Springer.
Cornelius, R. R. 1996. The science of emotion: Research and tradition in the psychology of emotions. Englewood Cliffs, NJ: Prentice Hall, Inc.
Disaster Relief and Material Security Division. 2022. “The ministry of emergency management released the national natural disaster situation in the first three quarters of 2022.” Accessed April 9, 2023. https://www.mem.gov.cn/xw/yjglbgzdt/202301/t20230113_440478.shtml.
Esparza, M., H. Farahmand, S. Brody, and A. Mostafavi. 2023. “Examining data imbalance in crowdsourced reports for improving flash flood situational awareness.” Int. J. Disaster Risk Reduct. 95 (Sep): 103825. https://doi.org/10.1016/j.ijdrr.2023.103825.
Fang, J., J. Hu, X. Shi, and L. Zhao. 2019. “Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm.” Int. J. Disaster Risk Reduct. 34 (Mar): 275–282. https://doi.org/10.1016/j.ijdrr.2018.11.027.
Geng, S., Q. Zhou, M. Li, D. Song, and Y. Wen. 2021. “Spatial–temporal differences in disaster perception and response among new media users and the influence factors: A case study of the Shouguang Flood in Shandong Province.” Nat. Hazards 105 (2): 2241–2262. https://doi.org/10.1007/s11069-020-04398-7.
Gul, S., T. A. Shah, M. Ahad, M. Mubashir, S. Ahmad, M. Gul, and S. Sheikh. 2018. “Twitter sentiments related to natural calamities: Analysing tweets related to the Jammu and Kashmir floods of 2014.” Electron. Libr. 36 (1): 38–54. https://doi.org/10.1108/EL-12-2015-0244.
Gupta, A., H. Lamba, P. Kumaraguru, and A. Joshi. 2013. “Faking sandy: Characterizing and identifying fake images on Twitter during Hurricane Sandy.” In Proc., 22nd Int. Conf. on World Wide Web-WWW, 729–736. New York: Association for Computing Machinery. https://doi.org/10.1145/2487788.2488033.
Han, X., and J. Wang. 2019. “Using social media to mine and analyze public sentiment during a disaster: A case study of the 2018 Shouguang city flood in China.” ISPRS Int. J. Geo-Inf. 8 (4): 185. https://doi.org/10.3390/ijgi8040185.
Huang, X., C. Wang, and Z. Li. 2018. “A near real-time flood-mapping approach by integrating social media and post-event satellite imagery.” Ann. GIS 24 (2): 113–123. https://doi.org/10.1080/19475683.2018.1450787.
Kent, J. D., and H. T. Capello Jr. 2013. “Spatial patterns and demographic indicators of effective social media content during the Horsethief Canyon fire of 2012.” Cartogr. Geogr. Inf. Sci. 40 (2): 78–89. https://doi.org/10.1080/15230406.2013.776727.
Khanmohammadi, S., E. Golafshani, Y. Bai, H. Li, M. Bazli, and M. Arashpour. 2023. “Multi-modal mining of crowd-sourced data: Efficient provision of humanitarian aid to remote regions affected by natural disasters.” Int. J. Disaster Risk Reduct. 96 (Oct): 103972. https://doi.org/10.1016/j.ijdrr.2023.103972.
Lavell, A., M. Oppenheimer, C. Diop, J. Hess, R. Lempert, J. Li, and S. Myeong. 2012. “Managing the risks of extreme events and disasters to advance climate change adaptation.” In Vol. 3 of A special report of working groups I and II of the intergovernmental panel on climate change (IPCC), 25–64. Cambridge, UK: Cambridge University Press.
Li, J. L., L. D. Cao, and R. L. Pu. 2014. “Progress on monitoring and assessment of flood disaster in remote sensing.” J. Hydraul. Eng. 45 (3): 253–260. https://doi.org/10.13243/j.cnki.slxb.2014.03.001.
Machine Learning and Artificial Intelligence Column. 2021. “Pearson correlation coefficient.” Accessed April 8, 2024. https://zhuanlan.zhihu.com/p/350334110.
Ma, M., Q. Gao, Z. Xiao, X. Hou, B. Hu, L. Jia, and W. Song. 2023. “Analysis of public emotion on flood disasters in southern China in 2020 based on social media data.” Nat. Hazards 118 (2): 1013–1033. https://doi.org/10.1007/s11069-023-06033-7.
Meerow, S., J. P. Newell, and M. Stults. 2016. “Defining urban resilience: A review.” Landscape Urban Plann. 147 (Mar): 38–49. https://doi.org/10.1016/j.landurbplan.2015.11.011.
Murzintcev, N., and C. Cheng. 2017. “Disaster hashtags in social media.” ISPRS Int. J. Geo-Inf. 6 (7): 204. https://doi.org/10.3390/ijgi6070204.
Neppalli, V. K., C. Caragea, A. Squicciarini, A. Tapia, and S. Stehle. 2017. “Sentiment analysis during Hurricane Sandy in emergency response.” Int. J. Disaster Risk Reduct. 21 (Mar): 213–222. https://doi.org/10.1016/j.ijdrr.2016.12.011.
Obinwanne, T., and P. Brandtner. 2024. “Enhancing sentiment analysis with GPT—A comparison of large language models and traditional machine learning techniques.” In Intelligent sustainable systems, edited by A. K. Nagar, D. S. Jat, D. Mishra, and A. Joshi, 187–197. Singapore: Springer.
Paradkar, A. S., C. Zhang, F. Yuan, and A. Mostafavi. 2022. “Examining the consistency between geo-coordinates and content-mentioned locations in tweets for disaster situational awareness: A Hurricane Harvey study.” Int. J. Disaster Risk Reduct. 73 (Apr): 102878. https://doi.org/10.1016/j.ijdrr.2022.102878.
People’s Government of Yingde City. 2022. “Statistical bulletin on national economic and social development of Yingde City, 2021.” Accessed September 6, 2023. https://www.yingde.gov.cn/zwgk/tjsj/content/post_1544797.html.
People’s Government of Yingde City. 2023. “Yingde City people’s government website.” Accessed January 20, 2024. http://www.yingde.gov.cn/.
Qu, Y., C. Huang, P. Zhang, and J. Zhang. 2011. “Microblogging after a major disaster in China: A case study of the 2010 Yushu earthquake.” In Proc., ACM 2011 Conf. on Computer Supported Cooperative Work: CSCW ’11, 25–34. New York: Association for Computing Machinery. https://doi.org/10.1145/1958824.1958830.
Said, N., K. Ahmad, A. Gul, N. Ahmad, and A. Al-Fuqaha. 2020. “Floods detection in twitter text and images.” Preprint, submitted November 20, 2020. http://arxiv.org/abs/2011.14943.
Sakaki, T., M. Okazaki, and Y. Matsuo. 2010. “Earthquake shakes twitter users: Real-time event detection by social sensors.” In Proc., 19th Int. Conf. on World Wide Web: WWW ’10, 851–860. New York: Association for Computing Machinery. https://doi.org/10.1145/1772690.1772777.
Sarirete, A. 2023. “Sentiment analysis tracking of COVID-19 vaccine through tweets.” J. Ambient Intell. Hum. Comput. 14 (11): 14661–14669. https://doi.org/10.1007/s12652-022-03805-0.
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Kilmov. 2017. “Proximal policy optimization algorithms.” Preprint, submitted July 20, 2017. http://arxiv.org/abs/1707.06347.
Slavkovikj, V., S. Verstockt, S. Van Hoecke, and R. Van de Walle. 2014. “Review of wildfire detection using social media.” Fire Saf. J. 68 (Aug): 109–118. https://doi.org/10.1016/j.firesaf.2014.05.021.
Untawale, T. M., and G. Choudhari. 2019. “Implementation of sentiment classification of movie reviews by supervised machine learning approaches.” In Proc., 3rd Int. Conf. on Computing Methodologies and Communication (ICCMC), 1197–1200. New York: IEEE. https://10.1109/ICCMC.2019.8819800.
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. “Attention is all you need.” Adv. Neural Inf. Process. Syst. 30 (Jun): 5998–6008. https://doi.org/10.48550/arXiv.1706.03762.
Wang, B., S. Liu, K. Ding, Z. Liu, and J. Xu. 2014. “Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: A case study in LTE technology.” Scientometrics 101 (1): 685–704. https://doi.org/10.1007/s11192-014-1342-3.
Wang, D., and R. Alfred. 2020. “A review on sentiment analysis model for Chinese Weibo text.” In Proc., 3rd Int. Conf. on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 456–463. New York: IEEE. https://doi.org/10.1109/AEMCSE50948.2020.00105.
Wang, Y., T. Wang, X. Ye, J. Zhu, and J. Lee. 2016. “Using social media for emergency response and urban sustainability: A case study of the 2012 Beijing rainstorm.” Sustainability 8 (1): 25. https://doi.org/10.3390/su8010025.
Wang, Z., and X. Ye. 2018. “Social Media Analytics for natural disaster management.” Int. J. Geogr. Inf. Sci. 32 (1): 49–72. https://doi.org/10.1080/13658816.2017.1367003.
Wankhade, M., A. C. S. Rao, and C. Kulkarni. 2022. “A survey on sentiment analysis methods, applications, and challenges.” Artif. Intell. Rev. 55 (7): 5731–5780. https://doi.org/10.1007/s10462-022-10144-1.
Wu, K., J. Wu, W. Ding, and R. Tang. 2021. “Extracting disaster information based on Sina Weibo in China: A case study of the 2019 Typhoon Lekima.” Int. J. Disaster Risk Reduct. 60 (Jun): 102304. https://doi.org/10.1016/j.ijdrr.2021.102304.
Wu, K., J. Wu, and Y. Li. 2022. “Mining typhoon victim information based on multi-source data fusion using social media data in China: A case study of the 2019 Super Typhoon Lekima.” Geomatics Nat. Hazards Risk 13 (1): 1087–1105. https://doi.org/10.1080/19475705.2022.2064774.
Xiao, Y., B. Li, and Z. Gong. 2018. “Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data.” Nat. Hazards 94 (2): 833–842. https://doi.org/10.1007/s11069-018-3427-4.
Yan, X., J. Guo, Y. Lan, and X. Cheng. 2013. “A biterm topic model for short texts.” In Proc., 22nd Int. Conf. on World Wide Web: WWW’13, 1445–1456. New York: Association for Computing Machinery. https://doi.org/10.1145/2488388.2488514.
Yang, T. F., J. B. Xie, Z. Y. Li, and G. Q. Li. 2018. “A method of typhoon disaster loss identification and classification using micro-blog information.” J. Geoinf. Sci. 20 (7): 906–917. https://doi.org/10.12082/dqxxkx.2018.180062.
Ye, J., et al. 2023. “A comprehensive capability analysis of GPT-3 and GPT-3.5 series models.” Preprint, submitted March 18, 2023. http://arxiv.org/abs/2303.10420.
Zhang, M., B. Xu, and J. Gong. 2015. “An anomaly detection model based on one-class SVM to detect network intrusions.” In Proc., 2015 11th Int. Conf. on Mobile Ad-hoc and Sensor Networks (MSN), 102–107. New York: IEEE. https://doi.org/10.1109/MSN.2015.40.
Zhang, S., Z. Wei, Y. Wang, and T. Liao. 2018. “Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary.” Future Gener. Comput. Syst. 81 (Apr): 395–403. https://doi.org/10.1016/j.future.2017.09.048.
Zhang, T., and C. Cheng. 2021. “Temporal and spatial evolution and influencing factors of public sentiment in natural disasters—A case study of Typhoon Haiyan.” ISPRS Int. J. Geo-Inf. 10 (5): 299. https://doi.org/10.3390/ijgi10050299.
Zhang, T., S. Shen, C. Cheng, K. Su, and X. Zhang. 2021. “A topic model based framework for identifying the distribution of demand for relief supplies using social media data.” Int. J. Geogr. Inf. Sci. 35 (11): 2216–2237. https://doi.org/10.1080/13658816.2020.1869746.
Zhu, R., D. Lin, M. Jendryke, C. Zuo, L. Ding, and L. Meng. 2019. “Geo-tagged social media data-based analytical approach for perceiving impacts of social events.” ISPRS Int. J. Geo-Inf. 8 (1): 15. https://doi.org/10.3390/ijgi8010015.
Information & Authors
Information
Published In
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Dec 9, 2023
Accepted: Jul 22, 2024
Published online: Sep 23, 2024
Published in print: Feb 1, 2025
Discussion open until: Feb 23, 2025
ASCE Technical Topics:
- Business management
- Case studies
- Disaster preparedness
- Disaster response
- Disaster risk management
- Disasters and hazards
- Emergency management
- Engineering fundamentals
- Floods
- Infrastructure
- Methodology (by type)
- Natural disasters
- Political factors
- Practice and Profession
- Public administration
- Public information programs
- Public opinion and participation
- Research methods (by type)
- Social factors
- Urban and regional development
- Water and water resources
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.