Abstract: Based on the actual data crawled from Sina Microblog, this paper mainly analyzes the growth law of three user characteristics: the number of followers, friends and statuses. They all increase linearly with time and the growth rate in round figures obeys the power-law distribution. It is found that these characteristics are mainly in sustainable and explosive growth patterns. Moreover, the user with the explosive growth pattern can be divided into four main categories, such as early-stage growth pattern, middle-stage growth pattern, later-stage growth pattern, and step-stage growth pattern. Furthermore, the users’ number of different growth patterns can be counted using the K-means clustering algorithm, which is based on the vector cosine similarity. The growth patterns of user characteristics are observed by cluster analysis of the actual time series, which are grouped by different sorting methods and initial scales. It is observed that the users with higher growth rate are mainly in explosive growth pattern, and the users with higher initial number tend to be in sustainable growth pattern. Finally, based on the analysis of the explosive growth process of the number of followers, the relationships between the growth of the numbers of retweet and comment are compared, and the reasons for the explosive growth of the users are proposed.

Key words: Microblog, growth patterns, cosine similarity, K-means clustering, time series