Publication Type

Journal Article

Publication Date

2-2016

Abstract

Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. 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, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro perspective. Frequent sub-graph mining is used to discover the information flow patterns of burst topic from the micro perspective. Experimental results show that several interesting burst patterns are discovered, which can reveal different burst topic clusters and frequent information flows of burst topic.

Keywords

Burst pattern, Burst topic, Frequent sub-graph mining, Hierarchical clustering

Discipline

Social Media

Research Areas

Data Management and Analytics

Publication

Computers and Electrical Engineering

First Page

1

Last Page

9

ISSN

0045-7906

Identifier

10.1016/j.compeleceng.2016.06.012

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1016/j.compeleceng.2016.06.012

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