Conference Proceeding Article
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.
TopicSketch, tweet stream, bursty topic, realtime
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Data Management and Analytics
IEEE 13th International Conference on Data Mining ICDM 2013: 7-10 Dec 2013, Dallas, Texas: Proceedings
City or Country
XIE, Wei; ZHU, Feida; JIANG, Jing; LIM, Ee Peng; and WANG, Ke.
TopicSketch: Real-time Bursty Topic Detection from Twitter. (2013). IEEE 13th International Conference on Data Mining ICDM 2013: 7-10 Dec 2013, Dallas, Texas: Proceedings. 837-846. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2115
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