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2019 | OriginalPaper | Buchkapitel

Topic-Level Bursty Study for Bursty Topic Detection in Microblogs

verfasst von : Yakun Wang, Zhongbao Zhang, Sen Su, Muhammad Azam Zia

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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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Titel: Topic-Level Bursty Study for Bursty Topic Detection in Microblogs
verfasst von: Yakun Wang
Zhongbao Zhang
Sen Su
Muhammad Azam Zia
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-16148-4_8

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