Online social streams has become a prominent channel for facilitating new sources of information and quick communication links, specifically during mass emergencies. The task of event burst detection focuses on monitoring Twitter streams, to identify emergent patterns based on tweet arrival rate. The vital features of rising sharply in frequency for modelling an event in text streams leads to burst activity. In this paper, a statistical approach is framed for Twitter streaming channel with a sudden change in the frequency of tweet showing a log-normal distribution of data arrival γ. The burstiness of event is framed as the Z-score of the tweet arrival rate λ which corresponds to the frequency of tweets delimited in the time window w. Moreover, the proposed approach identify events which generates a stratified representation of the set of keyword bursts. The experimental outcome determines a hierarchical composition of event detection on the diversified stream by choosing optimal parameters. © 2018 IEEE.