A Self-adaptive Clustering Scheme with a Time-Decay Function for Microblogging Text Mining

Online microblogging services such as Twitter allow users to post very short messages related to everything ranging from mundane daily life routines to breaking news events. This phenomenon has changed the way for information acquisition. In this paper, we present an instinctive method with a time-decay function which corresponds to the natural propagation of social networks for clustering real-time text streams collected from Twitter. Compared to most previous studies, we follow natural cascading behaviors of event lifecycle to develop a self-adaptive clustering model for online event detection. Also, we construct an expandable similarity matrix which is capable of evaluating microblogging posts with incomplete semantic features. Experimental results show that the proposed method is a sensible solution to monitoring momentous real-time events and utilizing the text streams to facilitate the management of social networking data.