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Multitask Learning for Sparse Failure Prediction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Topic-Level Bursty Study for Bursty Topic Detection in Microblogs

Authors : Yakun Wang, Zhongbao Zhang, Sen Su, Muhammad Azam Zia

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: 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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previous chapter Ensembles of Nested Dichotomies with Multiple Subset Evaluation
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Footnotes
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Metadata
Title
Topic-Level Bursty Study for Bursty Topic Detection in Microblogs
Authors
Yakun Wang
Zhongbao Zhang
Sen Su
Muhammad Azam Zia
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

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

Copyright Year: 2019

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

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