Burst Phenomenon Analysis in Social Tagging System using Hawkes Process

Abstract

In the activities of people on WEB such as SNS, the burst phenomenon is observed. Recently, Hawkes Process is used as a method to analyze the burst phenomenon. It is known that when the branching ratio, which is an index representing the internal dynamics of Hawkes Process, exceeds a certain threshold, the event time series transits from steady state to nonstationary state where the burst phenomena is likely to occur. In this paper, we focus on social tagging system among data obtained from SNS, and analyze how branching ratio changes timewise with service growth.