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Topic-Level Bursty Study for Bursty Topic Detection in Microblogs

  • Yakun Wang
  • Zhongbao Zhang
  • Sen SuEmail author
  • Muhammad Azam Zia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

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.

Keywords

Sina Weibo Bursty topic detection Topic model Hypothesis testing 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yakun Wang
    • 1
  • Zhongbao Zhang
    • 1
  • Sen Su
    • 1
    Email author
  • Muhammad Azam Zia
    • 1
    • 2
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.University of Agriculture FaisalabadFaisalabadPakistan

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