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Incremental and Adaptive Topic Detection over Social Media

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Database Systems for Advanced Applications (DASFAA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10827))

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Abstract

Social media like Twitter and Facebook are very popular nowadays for sharing users’ interests. However, the existing solutions on topic detection over social media overlook time and location factors, which are quite important and useful. Moreover, social media are frequently updated. Thus, the proposed detection model should handle the dynamic updates. In this paper, we introduce a topic model for topic detection that combines time and location. Our model is equipped with incremental estimation of the parameters of the topic model and adaptive window length according to the correlation of consecutive windows and their density. We have conducted extensive experiments to verify the effectiveness and efficiency of our proposed Incremental Adaptive Time Location (IncrAdapTL) model.

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Notes

  1. 1.

    http://twitter4j.org.

  2. 2.

    http://snowball.tartarus.org.

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Acknowledgment

The work is partially supported by the Hong Kong RGC GRF Project 16207617, National Grand Fundamental Research 973 Program of China under Grant 2014CB340303, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Webank Collaboration Research Project, and Microsoft Research Asia Collaborative Research Grant.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

    Konstantinos Giannakopoulos & Lei Chen

Authors
  1. Konstantinos Giannakopoulos
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  2. Lei Chen
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Corresponding authors

Correspondence to Konstantinos Giannakopoulos or Lei Chen .

Editor information

Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Giannakopoulos, K., Chen, L. (2018). Incremental and Adaptive Topic Detection over Social Media. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_30

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  • DOI: https://doi.org/10.1007/978-3-319-91452-7_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91451-0

  • Online ISBN: 978-3-319-91452-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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