The widespread practice of Online Social Networking leads to the diffusion of trending information and exchanging various opinions with socially connected people online. Social media steams data extracted from Social Networks has become a vital communication tool and also turn up as an eventual informative platform to catch real human voices at the time of emergency events like disaster. An effective underlying quantification model is proposed in this paper which uses change point detection algorithm to detect events based on the relative streaming tweet density - ratio respectively. A morphological time-series analysis is carried out determine the dissemination of information about crisis events using Information Entropy. Further, the Event - Link ratio (ELR) is estimated to obtain meaningful patterns in events been identified. This paper focus to empirically quantify the information dissemination of the events based on user's tweeting activities. The proposed quantification method is compared with state-of-art techniques in terms of event detection rate, the entropy of information spread. It is found that the accuracy of the proposed method is up to 94% with event detection after 75 seconds. K-Center Clustering (KCC) is used which results in the location detection accuracy of 85%.