TY - GEN
T1 - Trending words based event detection in Sina Weibo
AU - Lu, Xinjiang
AU - Yu, Zhiwen
AU - Guo, Bin
AU - Zhang, Jiafan
AU - Chin, Alvin
AU - Tian, Jilei
AU - Cao, Yang
N1 - Publisher Copyright:
© Copyright 2014 ACM.
PY - 2014/8/4
Y1 - 2014/8/4
N2 - Online social networks provide us an unprecedented volume of available data, which results from the pervasive adoption of online social applications. In particular, for the unique characteristics on promoting content sharing, microblogging social networks offer us a new proxy for detecting and tracking the events being taken place in the real world. In spite of large amount of social babble involved, the microblog data contains fresh news coming from human sensors at a humungous rate. As the online social network is a platform that is able to process fast changing streaming data, however it is hard to discover meaningful events in such noisy circumstances in time. In this paper, we study the keywords determining problem in event detection and propose a novel and much more effective method for discovering bursty words in microblogging social networks by leveraging temporal dynamics information. Based on this, we propose an efficient event detection framework applied in Sina Weibo-a Chinese microblogging site similar to Twitter. With experiments conducted on real data sourced from Sina Weibo, we show the effectiveness and feasibility of our proposed method and framework.
AB - Online social networks provide us an unprecedented volume of available data, which results from the pervasive adoption of online social applications. In particular, for the unique characteristics on promoting content sharing, microblogging social networks offer us a new proxy for detecting and tracking the events being taken place in the real world. In spite of large amount of social babble involved, the microblog data contains fresh news coming from human sensors at a humungous rate. As the online social network is a platform that is able to process fast changing streaming data, however it is hard to discover meaningful events in such noisy circumstances in time. In this paper, we study the keywords determining problem in event detection and propose a novel and much more effective method for discovering bursty words in microblogging social networks by leveraging temporal dynamics information. Based on this, we propose an efficient event detection framework applied in Sina Weibo-a Chinese microblogging site similar to Twitter. With experiments conducted on real data sourced from Sina Weibo, we show the effectiveness and feasibility of our proposed method and framework.
KW - Bursty words detection
KW - Event detection
KW - Sina Weibo
UR - https://www.scopus.com/pages/publications/84985914396
U2 - 10.1145/2640087.2644156
DO - 10.1145/2640087.2644156
M3 - 会议稿件
AN - SCOPUS:84985914396
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014
PB - Association for Computing Machinery
T2 - 3rd ASE International Conference on Big Data Science and Computing, BIGDATASCIENCE 2014
Y2 - 4 August 2014 through 7 August 2014
ER -