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Concept-based event identification from social streams using evolving social graph sequences

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Abstract

Social networks, which have become extremely popular in the twenty first 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” 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.

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Authors and Affiliations

  1. Department of Computer Science, Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan

    Yi-Shin Chen, Yi-Cheng Peng, Jheng-He Liang, Elvis Saravia, Fernando Calderon, Chung-Hao Chang, Ya-Ting Chuang, Tzu-Lung Chen & Elizabeth Kwan

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  1. Yi-Shin Chen
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  2. Yi-Cheng Peng
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  3. Jheng-He Liang
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  4. Elvis Saravia
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  5. Fernando Calderon
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  6. Chung-Hao Chang
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  8. Tzu-Lung Chen
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  9. Elizabeth Kwan
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Correspondence to Yi-Shin Chen.

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This article is part of the Social Network Analysis and Information Systems.

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Chen, YS., Peng, YC., Liang, JH. et al. Concept-based event identification from social streams using evolving social graph sequences. Soc. Netw. Anal. Min. 5, 30 (2015). https://doi.org/10.1007/s13278-015-0269-x

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