Abstract
Microblogging services, such as Twitter and Sina Weibo, have gained tremendous popularity in recent years. The huge amount of user-generated information is spread on microblogs. Such user-generated contents are a mixture of different bursty topics (e.g., breaking news) and general topics (e.g., user interests). However, it is challenging to discriminate between them due to the extremely diverse and noisy user-generated text. In this paper, we introduce a novel topic model to detect bursty topics from microblogs. Our model is based on an observation that different topics usually exhibit different bursty levels at a certain time. We propose to utilize the topic-level burstiness to differentiate bursty topics and non-bursty topics and particularly different bursty topics. Extensive experiments on a Sina Weibo Dataset show that our approach outperforms the baselines and the state-of-the-art method.
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Acknowledgements
This work was supported in part by the following funding agencies of China: National Key Research and Development Program of China under Grant 2016QY01W0200 and National Natural Science Foundation under Grant 61602050 and U1534201.
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Wang, Y., Zhang, Z., Su, S., Zia, M.A. (2019). Topic-Level Bursty Study for Bursty Topic Detection in Microblogs. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_8
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