Utilising social media data to evaluate urban flood impact in data scarce cities

Abstract

The growing amount of social media data is an invaluable and rapidly accessible source of information for flood response and recovery. In this study, a workflow framework is developed to assess urban flood impacts by extracting and analysing social media data, as well as identifying the intensive public response areas, using the case of 2020 China Chengdu rainstorm-induced flooding. A crawler-algorithm is applied to extract and filter the social media data from the commonly used social platforms, namely Weibo (static data) and Tiktok (dynamic data). Based on the spatiotemporal analysis, 232 flood sites with geological locations are identified. The study shows that, social media activities and precipitation are temporally correlated in a significant and positive way. The temporal evolution analysis of social media topics reveals the process of flooding and enables quick determination of severely affected areas. Spatially, social media data can provide spatial flood information and social media activities are typically connected with user demographics. Based on a flood simulation, the framework can generate valuable data sources of urban flooding from social media, which can enhance flood risk modelling with the aid of a hydrodynamic model. This study demonstrates the utility of social media in urban flooding impact assessment.