Social networks, which have become extremely popular in the 21st century, contain a tremendous amount of user-generated content about real-world events. This user-generated content relays real-world events as they happen, and sometimes even ahead of the newswire. The goal of this work is to identify events from social streams. The proposed model utilizes sliding-window-based statistical techniques to extract event candidates from social streams. Subsequently, the "Concept-based evolving graph sequences"(cEGS) approach is employed to verify information propagation trends of event candidates and to identify those events. The experimental results show the usefulness of our approach in identifying real-world events in social streams.