## 1 Introduction  
Natural disasters such as hurricanes, floods and tornadoes can cause significant life losses, property damages, and even political instability [1; 2; 3; 4]. In the context of global climate change, natural disasters have become more frequent and pose increasing physical, social, and economic threats to human society [5; 6]. Closely monitoring how a natural disaster disturbs human activities is thus of great value [7; 8; 9], particularly in an urban environment where human-environment relationships are usually much more complicated.  
Different methods and datasets have been used to study urban dwellers' response to a natural disaster. A comprehensive evaluation model was built to evaluate the macro-population vulnerability in response to an earthquake at city and county levels [10]. Questionnaire data were used to examine the impacts of bushfires on residents [11]. Qualitative studies have been conducted to examine how communities are resilient to the impacts of natural disasters using interview data [12].  
Natural disasters usually pose significant threats to the dwellers being inflicted. The threats vary dynamically. Consequently, people's emotion and public opinions to such threats may also change rapidly [13]. Such dynamic changes couldn't be timely captured and examined using the afore-mentioned conventional methods and data. Unfortunately, the dynamic changes of the human response to a disaster are extremely important in disaster evaluation and mitigation.  
The emerging social media data have shown their usefulness in tracking human behaviors in response to natural disasters and emergencies. Nowadays, Twitter, Facebook, Flicker and Weibo have become indispensable platforms for people to share their ideas and disseminate vital information in time [14; 15; 16], in particularly when a natural disaster, public safety event, or disease infection event occurs [17; 18; 19; 20; 21]. Data collected through these platforms have been used in improving situation awareness [22; 23; 24], event detection [25; 26], communication analysis [27; 28] and even helping governments guide the public opinions [29; 30].  
Social media data have also been used to detect and monitor the ongoing development of disasters such as influenza transmission, floods, typhoons, hurricanes, and terrorist attacks [31; 32; 33; 34]. Different social media data have been examined to reveal the different development stages of a disaster and how human response to the development stages [35; 36; 37]. A research framework was constructed to extract multidimensional (time, space, content, and network) information from social data [8]. Disaster-related information derived from social media data was also used to examine the spatiotemporal impacts of a disaster on human activities and assess the actual disaster-induced damages [38; 39; 40]. A variety of machine learning methods have also been used to examine social media data to evaluate the disaster-induced damages [41; 42].  
Many studies have shown that public opinions in the virtual space, as reflected by social media data, is consistent with what happens in the real world at a broad geographical scale. Such consistency has not been sufficiently examined and clearly illustrated at a finer temporal and spatial scale. There is also a need to study the factors driving the human response to a disaster, particularly in an urban environment. In this study, we investigated how dwellers in a mega city react to rainstorms as revealed by blogging activities on Weibo, one of the most popular social media platforms in China. We then explored various factors that may contribute to the blogging activities in response to rainstorms.