In recent years, social vulnerability has gained much importance in academic studies. However, social indices are rarely combined and validated with exposure and resilience components. This study provides an integrated analysis of the flood exposure and social vulnerability of rural households in a case area of Charsadda District, Khyber Pakhtunkhwa, Pakistan. A conceptual framework was designed (based on the MOVE framework) as a guideline and key indicators were identified. For the exposure component, parameters such as elevation, flooded locations, and distance from the river were endorsed to understand flood mechanisms. For populating socioeconomic variables, questionnaire-based interviews were conducted with 210 households. The results were presented through ArcGIS-generated maps. The most significant indicators interplaying with high vulnerability were exposure-related indicators. The findings showed that the southern areas, including Agra, Daulat Pura, and Hisar Yasinzai were highly vulnerable due to having the highest number of flood locations, lowest elevations, and shortest distances from rivers, as well as larger household sizes, more elderly, children and women, illiteracy rates, and weak financial capacity. Understanding such dominant indicators and areas where high social vulnerability and high exposure converge can inform the authorities in mitigating both social and physical flood vulnerability.

Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed.