Incremental and Adaptive Topic Detection over Social Media

  • Konstantinos Giannakopoulos
  • Lei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


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.



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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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyKowloonHong Kong

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