TY - GEN
T1 - Discovering event evolution chain in microblog
AU - Lu, Zhongyu
AU - Yu, Weiren
AU - Zhang, Richong
AU - Li, Jianxin
AU - Wei, Hua
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/23
Y1 - 2015/11/23
N2 - Microblog, such as Weibo and Twitter, has become an important platform where people share their opinions. Much research has been done to detect topics and events in microblogs. Due to the dynamic nature of events, it is more crucial to monitor the evolution and trace the development of the events. People pay more attention to the whole evolution chain of the events rather than a single event. In this paper, we propose a method to automatically discover event evolution chain in microblogs based on multiple similarity measures including contents, locations and participants. We build a 5-tuple event description model specifically for events detected from microblogs and analyze their relationships. Inverted index and locality-sensitive hashing are used to improve the efficiency of the algorithm. Experiment shows that our method gain a 143.33% speed up against method without locality-sensitive hashing. In comparison with the ground truth and a baseline method, the result illustrates that it effectively covers ground truth and outperforms the baseline method especially in dealing with the long-term spanning events.
AB - Microblog, such as Weibo and Twitter, has become an important platform where people share their opinions. Much research has been done to detect topics and events in microblogs. Due to the dynamic nature of events, it is more crucial to monitor the evolution and trace the development of the events. People pay more attention to the whole evolution chain of the events rather than a single event. In this paper, we propose a method to automatically discover event evolution chain in microblogs based on multiple similarity measures including contents, locations and participants. We build a 5-tuple event description model specifically for events detected from microblogs and analyze their relationships. Inverted index and locality-sensitive hashing are used to improve the efficiency of the algorithm. Experiment shows that our method gain a 143.33% speed up against method without locality-sensitive hashing. In comparison with the ground truth and a baseline method, the result illustrates that it effectively covers ground truth and outperforms the baseline method especially in dealing with the long-term spanning events.
KW - Event Evolution
KW - Inverted Index
KW - Locality-Sensitive Hashing
KW - Microblog
KW - Similarity Measure
UR - https://www.scopus.com/pages/publications/84961728586
UR - https://www.scopus.com/pages/publications/84961728586#tab=citedBy
U2 - 10.1109/HPCC-CSS-ICESS.2015.81
DO - 10.1109/HPCC-CSS-ICESS.2015.81
M3 - Conference contribution
AN - SCOPUS:84961728586
T3 - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015
SP - 635
EP - 640
BT - Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on High Performance Computing and Communications, IEEE 7th International Symposium on Cyberspace Safety and Security and IEEE 12th International Conference on Embedded Software and Systems, HPCC-ICESS-CSS 2015
Y2 - 24 August 2015 through 26 August 2015
ER -