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The exploding popularity of social networks provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to floods in 2020. The spatial distribution of floods was analyzed using Kernel Density Estimation. Public emotion was analyzed using Natural Language Processing tools. The association between floods and public emotion was explored through correlation analysis methods. The results indicated that: (1) Weibo texts could be utilized as effective data to identify urban waterlogging risk in Guangdong and Guangxi. (2) The waterlogging was mainly distributed in the southern part of Guangdong and Guangxi, especially in the provincial capitals and coastal cities. (3) Public emotion was predominantly negative, especially during periods of heavy precipitation. (4) There was a strong correlation between public emotion and floods in spatial–temporal variation. The degree of negative public emotion was significantly influenced by the number of waterlogging points. The presented results serve as the preliminary data for future planning and designing of emergency management. [ABSTRACT FROM AUTHOR]

• We compared the structural characteristics of 18 urban agglomerations in China. • Our framework contained analysis of 3 different scales based on 4 elements' flows. • Guangdong-Hong Kong-Macao urban Agglomeration had the greatest average flow (5.15). • Shandong Peninsula Urban Agglomeration had the best performance in overall network. • Cities with high centrality were in the bidirectional or main spillover subgroups. Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into "main spillover" and "bidirectional spillover" subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]