Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.

Traditional space–time paths show the spatiotemporal trajectories of individuals in one to several days. Based on data for such short periods, these space–time paths might not be able to show regular activity patterns, which are pertinent to various types of planning and policy analysis. Travel data gathered for longer periods might capture regular activity patterns, but footprints captured by these data also include irregular activities, introducing noises or uncertainty. Our objective is to determine the representative spatiotemporal trajectories of individuals, accounting for stochastic disturbances and spatiotemporal variability, but using activity data with longer duration. Therefore, we explore using Twitter data, which have relatively low and irregular spatial and temporal resolutions. This article introduces a methodology to construct individual representative space–time paths using various aggregation and spatiotemporal clustering techniques. To depict and visualize spatiotemporal trajectories with uncertain information, we propose space–time cones of variable sizes to reflect the spatial precision of the paths and use colors on the cones to represent the confidence level. To illustrate the proposed methodology, we use the geo-tagged tweets for an extended period. Our analysis indicates that the representative space–time path reasonably describes an individual's regular activity patterns. As visual elements, cones and cone colors effectively show the varying geographical precision along the path and changing certainty levels across different path segments, respectively.