Event identification for social streams using keyword-based evolving graph sequences

Authors:
Elizabeth Kwan;Pei-Ling Hsu;Jheng-He Liang;Yi-Shin Chen
Affiliations:
National Tsing Hua University, Taiwan;National Tsing Hua University, Taiwan;National Tsing Hua University, Taiwan;National Tsing Hua University, Taiwan
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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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.