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2013 IEEE 13th International Conference on Data Mining (2013)
Dallas, TX, USA USA
Dec. 7, 2013 to Dec. 10, 2013
ISSN: 1550-4786
pp: 837-846
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 Topic Sketch, 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 Topic Sketch 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.
Real-time systems, Twitter, Acceleration, Equations, Surges, Monitoring, Optimization,realtime, TopicSketch, tweet stream, bursty topic
Wei Xie, Feida Zhu, Jing Jiang, Ee-Peng Lim, Ke Wang, "TopicSketch: Real-Time Bursty Topic Detection from Twitter", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 837-846, 2013, doi:10.1109/ICDM.2013.86
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