Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2012
Abstract
Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments on a large Twitter dataset shows our methods are very effective.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
First Page
1466
Last Page
1477
City or Country
Jeju Island, Korea
Citation
ZHAO, Xin; Shu, Baihan; JIANG, Jing; SONG, YANG; YAN, Hongfei; and LI, Xiaoming.
Identifying Event-related Bursts via Social Media Activities. (2012). EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 1466-1477.
Available at: https://ink.library.smu.edu.sg/sis_research/1621
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://www.aclweb.org/anthology-new/D/D12/D12-1134.pdf
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons