{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:21:43Z","timestamp":1767846103285,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72242106"],"award-info":[{"award-number":["72242106"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023BTY128"],"award-info":[{"award-number":["2023BTY128"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["24YTC038"],"award-info":[{"award-number":["24YTC038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Social Science Foundation of Xinjiang Uygur Autonomous Region","award":["72242106"],"award-info":[{"award-number":["72242106"]}]},{"name":"Project of Social Science Foundation of Xinjiang Uygur Autonomous Region","award":["2023BTY128"],"award-info":[{"award-number":["2023BTY128"]}]},{"name":"Project of Social Science Foundation of Xinjiang Uygur Autonomous Region","award":["24YTC038"],"award-info":[{"award-number":["24YTC038"]}]},{"name":"Beijing Social Science Foundation","award":["72242106"],"award-info":[{"award-number":["72242106"]}]},{"name":"Beijing Social Science Foundation","award":["2023BTY128"],"award-info":[{"award-number":["2023BTY128"]}]},{"name":"Beijing Social Science Foundation","award":["24YTC038"],"award-info":[{"award-number":["24YTC038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Extreme rainfall events are significant manifestations of climate change, causing substantial impacts on urban infrastructure and public life. This study takes the extreme rainfall event in Beijing in 2023 as the background and utilizes data from Sina Weibo. Based on large language models and prompt engineering, disaster information is extracted, and a multi-factor coupled disaster multi-sentiment classification model, Bert-BiLSTM, is designed. A disaster analysis framework focusing on three dimensions of theme, location and sentiment is constructed. The results indicate that during the pre-disaster stage, themes are concentrated on warnings and prevention, shifting to specific events and rescue actions during the disaster, and post-disaster, they express gratitude to rescue personnel and highlight social cohesion. In terms of spatial location, the disaster shows significant clustering, predominantly occurring in Mentougou and Fangshan. There is a clear difference in emotional expression between official media and the public; official media primarily focuses on neutral reporting and fact dissemination, while public sentiment is even richer. At the same time, there are also variations in sentiment expressions across different affected regions. This study provides new perspectives and methods for analyzing extreme rainfall events on social media by revealing the evolution of disaster themes, the spatial distribution of disasters, and the temporal and spatial changes in sentiment. These insights can support risk assessment, resource allocation, and public opinion guidance in disaster emergency management, thereby enhancing the precision and effectiveness of disaster response strategies.<\/jats:p>","DOI":"10.3390\/ijgi14040136","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T12:01:50Z","timestamp":1742817710000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Multidimensional Study of the 2023 Beijing Extreme Rainfall: Theme, Location, and Sentiment Based on Social Media Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5502-2974","authenticated-orcid":false,"given":"Xun","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China"},{"name":"Key Laboratory of Earthquake Forecasting and Risk Assessment, Ministry of Emergency Management, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdureyim","family":"Raxidin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Xinjiang HeTian College, Hotan 848000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104144","DOI":"10.1016\/j.advwatres.2022.104144","article-title":"Extreme precipitation in China: A review on statistical methods and applications","volume":"163","author":"Gu","year":"2022","journal-title":"Adv. Water Resour."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/s41612-024-00584-7","article-title":"Locally opposite responses of the 2023 Beijing\u2013Tianjin\u2013Hebei extreme rainfall event to global anthropogenic warming","volume":"7","author":"Zhao","year":"2024","journal-title":"NPJ Clim. Atmos. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1080\/17538947.2023.2239768","article-title":"Improving social media use for disaster resilience: Challenges and strategies","volume":"16","author":"Lam","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100295","DOI":"10.1016\/j.osnem.2024.100295","article-title":"Harnessing prompt-based large language models for disaster monitoring and automated reporting from social media feedback","volume":"45","author":"Cantini","year":"2025","journal-title":"Online Soc. Netw. 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Biomimetics, 9.","DOI":"10.3390\/biomimetics9090533"},{"key":"ref_8","first-page":"470","article-title":"Sentiment analysis of flood disaster management in Jakarta on Twitter using support vector machines","volume":"7","author":"Saddam","year":"2023","journal-title":"Sink. J. Dan Penelit. Tek. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102289","DOI":"10.1016\/j.ijinfomgt.2020.102289","article-title":"Social media for enhanced understanding of disaster resilience during Hurricane Florence","volume":"57","author":"Yuan","year":"2021","journal-title":"Int. J. Inf. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105170","DOI":"10.1016\/j.ijdrr.2024.105170","article-title":"Assessment of urban flood disaster responses and causal analysis at different temporal scales based on social media data and machine learning algorithms","volume":"117","author":"Guo","year":"2025","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"05024015","DOI":"10.1061\/NHREFO.NHENG-2107","article-title":"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","volume":"26","author":"Li","year":"2025","journal-title":"Nat. Hazards Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"174948","DOI":"10.1016\/j.scitotenv.2024.174948","article-title":"An integrated framework for flood disaster information extraction and analysis leveraging social media data: A case study of the Shouguang flood in China","volume":"949","author":"Hou","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106021","DOI":"10.1016\/j.envsoft.2024.106021","article-title":"Spatiotemporal assessment of urban flooding hazard using social media: A case study of Zhengzhou \u20187\u00b720\u2019","volume":"176","author":"Peng","year":"2024","journal-title":"Environ. Model. Softw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104175","DOI":"10.1016\/j.ijdrr.2023.104175","article-title":"Spatio-temporal evolution of public opinion on urban flooding: Case study of the 7.20 Henan extreme flood event","volume":"100","author":"Wang","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104422","DOI":"10.1016\/j.ijdrr.2024.104422","article-title":"Research on online public opinion in the investigation of the \u201c7\u201320\u201d extraordinary rainstorm and flooding disaster in Zhengzhou, China","volume":"105","author":"Zhang","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105056","DOI":"10.1016\/j.ijdrr.2024.105056","article-title":"Disaster information mining from a social perception perspective: A case study of the \u201c23\u00b7 7\u201d extreme rainfall event in the Beijing\u2013Tianjin\u2013Hebei region","volume":"115","author":"Wang","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105077","DOI":"10.1016\/j.scs.2023.105077","article-title":"Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media","volume":"101","author":"Yan","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qian, J., Du, Y., Liang, F., Yi, J., Wang, N., Tu, W., Huang, S., Pei, T., and Ma, T. (2024). Quantifying urban linguistic diversity related to rainfall and flood across China with social media data. ISPRS Int. J. Geo-Inf., 13.","DOI":"10.3390\/ijgi13030092"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, Y., Yang, B., He, H., Fei, X., Fan, X., and Liu, J. (2024). Event Argument Extraction for Rainstorm Disasters Based on Social Media: A Case Study of the 2021 Heavy Rains in Henan. Water, 16.","DOI":"10.3390\/w16233535"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1007\/s00530-022-00956-0","article-title":"Bert-based semi-supervised domain adaptation for disastrous classification","volume":"28","author":"Wang","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2348668","DOI":"10.1080\/17538947.2024.2348668","article-title":"Multi-class multi-label classification of social media texts for typhoon damage assessment: A two-stage model fully integrating the outputs of the hidden layers of BERT","volume":"17","author":"Zou","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10417","DOI":"10.1007\/s12652-022-03698-z","article-title":"Employing BERT-DCNN with sentic knowledge base for social media sentiment analysis","volume":"14","author":"Jain","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"130128","DOI":"10.1016\/j.jhydrol.2023.130128","article-title":"An approach of using social media data to detect the real time spatio-temporal variations of urban waterlogging","volume":"625","author":"Chen","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103609","DOI":"10.1016\/j.ipm.2023.103609","article-title":"Emotion-cognitive reasoning integrated BERT for sentiment analysis of online public opinions on emergencies","volume":"61","author":"Wan","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"526","DOI":"10.5573\/IEIESPC.2023.12.6.526","article-title":"Unveiling the Power of Deep Learning: A Comparative Study of LSTM, BERT, and GRU for Disaster Tweet Classification","volume":"12","author":"Ullah","year":"2023","journal-title":"IEIE Trans. Smart Process. Comput."},{"key":"ref_26","first-page":"477","article-title":"Analysis of public sentiment tendency in sudden meteorological disasters based on LSTM-BLS","volume":"13","author":"Luo","year":"2021","journal-title":"Nanjing Xinxi Gongcheng Daxue Xuebao"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1002\/spe.2851","article-title":"Spatiotemporal-based sentiment analysis on tweets for risk assessment of event using deep learning approach","volume":"51","author":"Parimala","year":"2021","journal-title":"Softw. Pract. Exp."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"46538","DOI":"10.1109\/ACCESS.2022.3170897","article-title":"A deep attentive multimodal learning approach for disaster identification from social media posts","volume":"10","author":"Hossain","year":"2022","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.29207\/resti.v6i6.4525","article-title":"Using social media data to monitor natural disaster: A multi dimension convolutional neural network approach with word embedding","volume":"6","author":"Faisal","year":"2022","journal-title":"J. RESTI (Rekayasa Sist. Dan Teknol. Inf.)"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102482","DOI":"10.1016\/j.ijdrr.2021.102482","article-title":"Social media data-based typhoon disaster assessment","volume":"64","author":"Chen","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_31","first-page":"2069","article-title":"Study on typhoon disaster assessment by mining data from social media based on artificial neural network","volume":"116","author":"Li","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hassan, S.Z., Ahmad, K., Hicks, S., Halvorsen, P., Al-Fuqaha, A., Conci, N., and Riegler, M. (2022). Visual sentiment analysis from disaster images in social media. Sensors, 22.","DOI":"10.3390\/s22103628"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"68917","DOI":"10.1109\/ACCESS.2021.3074819","article-title":"Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review","volume":"9","author":"Dwarakanath","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1007\/s10796-022-10309-x","article-title":"Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster","volume":"25","author":"Kumar","year":"2023","journal-title":"Inf. Syst. Front."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"41405","DOI":"10.1007\/s11042-023-16766-z","article-title":"Utilizing social media for emergency response: A tweet classification system using attention-based BiLSTM and CNN for resource management","volume":"83","author":"Koshy","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"241","DOI":"10.35882\/jeeemi.v5i4.319","article-title":"LSTM and Bi-LSTM models for identifying natural disasters reports from social media","volume":"5","author":"Yunida","year":"2023","journal-title":"J. Electron. Electromed. Eng. Med. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5768","DOI":"10.1007\/s10489-024-05462-6","article-title":"Applying social media in emergency response: An attention-based bidirectional deep learning system for location reference recognition in disaster tweets","volume":"54","author":"Koshy","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s11069-021-05081-1","article-title":"Early detection of emergency events from social media: A new text clustering approach","volume":"111","author":"Huang","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117581","DOI":"10.1016\/j.eswa.2022.117581","article-title":"MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis","volume":"203","author":"Pimpalkar","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1111\/tgis.13097","article-title":"Social media insights on public perception and sentiment during and after disasters: The European floods in 2021 as a case study","volume":"27","author":"Li","year":"2023","journal-title":"Trans. GIS"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s13753-022-00442-1","article-title":"Disaster impacts surveillance from social media with topic modeling and feature extraction: Case of Hurricane Harvey","volume":"13","author":"Mihunov","year":"2022","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"121307","DOI":"10.1016\/j.eswa.2023.121307","article-title":"Evolution of online public opinions on major accidents: Implications for post-accident response based on social media network","volume":"235","author":"Zhou","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107093","DOI":"10.1016\/j.eiar.2023.107093","article-title":"Environmental disaster and public rescue: A social media perspective","volume":"100","author":"Li","year":"2023","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104893","DOI":"10.1016\/j.cageo.2021.104893","article-title":"Disaster damage assessment based on fine-grained topics in social media","volume":"156","author":"Dou","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1080\/13658816.2020.1869746","article-title":"A topic model based framework for identifying the distribution of demand for relief supplies using social media data","volume":"35","author":"Zhang","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"123604","DOI":"10.1016\/j.eswa.2024.123604","article-title":"SatCoBiLSTM: Self-attention based hybrid deep learning framework for crisis event detection in social media","volume":"249","author":"Upadhyay","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Song, G., and Huang, D. (2021). A sentiment-aware contextual model for real-time disaster prediction using twitter data. Future Internet, 13.","DOI":"10.3390\/fi13070163"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102701","DOI":"10.1016\/j.ijdrr.2021.102701","article-title":"Examining Community Vulnerabilities through multi-scale geospatial analysis of social media activity during Hurricane Irma","volume":"68","author":"Forati","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Li, Y., Lan, J., and Hamidi, A.R. (2021). Utilizing user-generated content and gis for flood susceptibility modeling in mountainous areas: A case study of Jian City in China. Sustainability, 13.","DOI":"10.3390\/su13126929"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104574","DOI":"10.1016\/j.ijdrr.2024.104574","article-title":"Enhanced Earthquake Impact Analysis based on Social Media Texts via Large Language Model","volume":"109","author":"Han","year":"2024","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Otal, H.T., Stern, E., and Canbaz, M.A. (2024, January 25\u201327). Llm-assisted crisis management: Building advanced llm platforms for effective emergency response and public collaboration. Proceedings of the 2024 IEEE Conference on Artificial Intelligence, Singapore.","DOI":"10.1109\/CAI59869.2024.00159"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Murzintcev, N., and Cheng, C. (2017). Disaster hashtags in social media. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6070204"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cheng, C., Zhang, T., Su, K., Gao, P., and Shen, S. (2019). Assessing the intensity of the population affected by a complex natural disaster using social media data. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8080358"},{"key":"ref_54","unstructured":"Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., and Huang, F. (2024). Qwen2 technical report. arXiv."},{"key":"ref_55","first-page":"246","article-title":"Improving sentiment analysis accuracy with emoji embedding","volume":"2","author":"Liu","year":"2021","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_56","first-page":"421","article-title":"Topical and emotional expressions regarding extreme weather disasters on social media: A comparison of posts from official media and the public","volume":"9","author":"Han","year":"2022","journal-title":"Humanit. Soc. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/4\/136\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:59:16Z","timestamp":1760029156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/14\/4\/136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,24]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["ijgi14040136"],"URL":"https:\/\/doi.org\/10.3390\/ijgi14040136","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,24]]}}}