Exploring the utility of social media data for urban flood impact assessment in data scarce cities

Abstract. The growing amount of social media data is a valuable and rapidly available information source to inform 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 and the identified 232 flood sites with geological locations, the study shows that, social media activities and precipitation have a significant positive correlation temporally. The temporal evolution analysis of social media topics reveals the process of flooding enabling quickly to determine the severely affected areas. Spatially, social media data can give spatial flood information and social media activities are generally associated with the demographical distribution of users. Based on a flood simulation, the framework can generate reliable data source of urban flooding from social media, which can enhance flood risk modelling with the aid of hydrodynamic model. This study demonstrates the utility of social media data for urban flood assessment.