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

  • Yi-Shin Chen
  • Yi-Cheng Peng
  • Jheng-He Liang
  • Elvis Saravia
  • Fernando Calderon
  • Chung-Hao Chang
  • Ya-Ting Chuang
  • Tzu-Lung Chen
  • Elizabeth Kwan
Original Article
Part of the following topical collections:
  1. Social Network Analysis and Information Systems

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.

Keywords

Event identification Concept-based evolving graph sequences Social networks 

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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Yi-Shin Chen
    • 1
  • Yi-Cheng Peng
    • 1
  • Jheng-He Liang
    • 1
  • Elvis Saravia
    • 1
  • Fernando Calderon
    • 1
  • Chung-Hao Chang
    • 1
  • Ya-Ting Chuang
    • 1
  • Tzu-Lung Chen
    • 1
  • Elizabeth Kwan
    • 1
  1. 1.Department of Computer Science, Institute of Information Systems and ApplicationsNational Tsing Hua UniversityHsinchuTaiwan

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