Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

12-2013

Abstract

Twitter has become one of the largest platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modeling and analysis in Twitter, it remains a huge challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper TopicSketch, a novel sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that TopicSketch can potentially handle hundreds of millions tweets per day which is close to the total number of daily tweets in Twitter and present bursty event in finer-granularity.

Keywords

TopicSketch, tweet stream, bursty topic, realtime

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Management and Analytics

Publication

IEEE 13th International Conference on Data Mining ICDM 2013: 7-10 Dec 2013, Dallas, Texas: Proceedings

First Page

837

Last Page

846

ISBN

9780769551081

Identifier

10.1109/ICDM.2013.86

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

LARC

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/10.1109/ICDM.2013.86