Citation: | DONG Guozhong, LI Ruiguang, YANG Wu, et al., “Microblog Burst Keywords Detection Based on Social Trust and Dynamics Model,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 695-700, 2014, |
S. Nepal, et al., A trust model-based analysis of social networks, International Journal of Trust Management in Computing and Communications, Vol.1, No.1, pp.3-22, 2013.
|
J. Caverlee, et al., The socialtrust framework for trusted social information management: Architecture and algorithms, Information Sciences, Vol.180, No.1, pp.95-112, 2010.
|
J. Kleinberg, Bursty and hierarchical structure in streams, Data Mining and Knowledge Discovery, Vol.7, No.4, pp.373-397, 2003.
|
L. Chen and A. Roy, Event detection from flickr data through wavelet-based spatial analysis, Conference on Information and Knowledge Management, Hong Kong, China, pp.523-532, 2009.
|
J. Guzman and B. Poblete, On-line relevant anomaly detection in the Twitter stream: An efficient bursty keyword detection model, the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, USA, pp.31-39, 2013.
|
Z. Zhang, M. Xu and N. Zheng, Mining burst topical keywords from microblog stream, International Conference on Computer Science and Network Technology, Changchun, China, pp.1760-1765, 2012.
|
T. Lappas, M.R. Vieira, D. Gunopulos and V.J. Tsotras, On the spatiotemporal burstiness of terms, Proceedings of the VLDB Endowment, Vol.5, No.9, pp.836-847, 2012.
|
S. Jing, Y. Jianping, W. Ting and L. Yan, A novel model of bursts in event sequences, International Conference on Consumer Electronics, Communications and Networks, Yichang, China, pp.816-821, 2012.
|
Y. Du, W. Wu, Y. He and N. Liu, Microblog bursty feature detection based on dynamics model, International Conference on Systems and Informatics, Yantai, China, pp.2304-2308, 2012.
|
Q. Diao, J. Jiang, F. Zhu and E.-P. Lim, Finding bursty topics from microblogs, the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Korea, pp.536-544, 2012.
|
J. Yao, et al., Temporal and social context based burst detection from folksonomies, the 24th AAAI Conference on Artificial Intelligence, Atlanta, USA, pp.1474-1479, 2010.
|
W. Chen, et al., Online detection of bursty events and their evolution in news streams, Journal of Zhejiang University Science C, Vol.11, No.5, pp.340-355, 2010.
|
Q. He, K. Chang and E.-P. Lim, Analyzing feature trajectories for event detection, the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, The Netherlands, pp.207-214, 2007.
|
D. He and D.S. Parker, Topic dynamics: An alternative model of bursts in streams of topics, the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, USA, pp.443-452, 2010.
|