Lifecycle-Based Event Detection from Microblogs

Abstract

Microblog like Twitter and Sina Weibo has been an important information source for event detection and monitoring. In many decision-making scenarios, it is not enough to only provide a structural tuple for an event, e.g., a 5W1H record like. However, in addition to event structural tuples, people need to know the evolution lifecycle of an event. The lifecycle description of an event is more helpful for decision making because people can focus on the progress and trend of events. In this paper, we propose a novel method for efficiently detecting and tracking event evolution on microblogging platforms. The major features of our study are: (1) It provides a novel event-type-driven method to extract event tuples, which forms the foundation for event evolution analysis. (2) It describes the lifecycle of an event by a staged model, and provides effective algorithms for detecting the stages of an event. (3) It offers emotional analysis over the stages of an event, through which people are able to know the public emotional tendency over a specific event at different time periods. We build a prototype system and present its architecture and implemental details in the paper. In addition, we conduct experiments on real microblog datasets and the results in terms of precision, recall, and F-measure suggest the effectiveness and efficiency of our proposal.