Space, time, and disease on social media: a case study of dengue fever in China

Abstract

It is possible to generate real-time and location-by-location data of many types of human dynamic events based on social media information for the awareness of events in public health. Analyzing these events is useful in understanding spatiotemporal trends and patterns of how diseases spread and also provides indications for users’ sentiment about the concerned disease. This article examines the spatial and temporal patterns of social media posts based on the content, attributes, and follower activities of posts on social media. We describe the spatial features of the topic discussed in the posts and the spatial relationship among comments on the posts. We present models for describing the diffusion process of these posts and for exploring their spatiotemporal patterns. Our results suggest that (1) the long-term trends of the topics in users’ views seem to be stable, (2) results from analyzing follower activities of posts are critical in describing the spatial patterns of the posts, and (3) the diffusion process of an event in social media is still similar to that of a traditional information diffusion model. Our findings are useful for understanding social media and social events. The processes we describe in this article suggest a standard form of analysis that can be adopted for extracting spatiotemporal patterns of information diffusion and for data mining in social media posts.