Abstract
Social networks, which have become extremely popular nowadays, contain a tremendous amount of user-generated content about real-world events. This user-generated content can naturally reflect the real-world event as they happen, and sometimes even ahead of the newswire. The goal of this work is to identify events from social streams. A model called keyword-based evolving graph sequences (kEGS) is proposed to capture the characteristics of information propagation in social streams. The experimental results show the usefulness of our approach in identifying real-world events in social streams.
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