Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2016
Abstract
Twitter has become one of largest social networks for users to broad-cast burst topics. Influential users usually have a large number of followers and play an important role in the diffusion of burst topic. There have been many studies on how to detect influential users. However, traditional influential users detection approaches have largely ignored influential users in user community. In this paper, we investigate the problem of detecting community pacemakers. Community pacemakers are defined as the influential users that promote early diffusion in the user community of burst topic. To solve this problem, we present DCPBT, a framework that can detect community pacemakers in burst topics. In DCPBT, a burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, a user community detection algorithm based on random walk is applied to discover user community. For large-scale user community, we propose a ranking method to detect community pacemakers in each large-scale user community. To test our framework we conduct the framework over Twitter burst topic detection system. Experimental results show that our method is more effective to detect the users that influence other users and promote early diffusion in the early stages of burst topic.
Keywords
Twitter, Burst topic, User graph model, Community pacemakers
Discipline
Databases and Information Systems | Social Media
Research Areas
Data Science and Engineering
Publication
APWeb 2016: Proceedings of the 18th Asia Pacific Web Conference: Suzhou, China, 2016 September 23-25
Volume
9931
First Page
245
Last Page
255
ISBN
9783319458144
Identifier
10.1007/978-3-319-45814-4_20
Publisher
Springer
City or Country
Cham
Citation
DONG, Guozhong; YANG, Wu; ZHU, Feida; and WANG, Wei.
Detecting community pacemakers of burst topic in Twitter. (2016). APWeb 2016: Proceedings of the 18th Asia Pacific Web Conference: Suzhou, China, 2016 September 23-25. 9931, 245-255.
Available at: https://ink.library.smu.edu.sg/sis_research/3447
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-319-45814-4_20