Top-K Spatio-Topic Query on Social Media Data

  • Lianming Zhou
  • Xuanhao Chen
  • Yan Zhao
  • Kai ZhengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11447)


With the development of social media and GPS-enabled devices, people can search for what they are interested in more easily. There are many methods, such as spatial keyword query, proposed to help people get useful information. However, most existing methods are based on location and keywords query which neglect the semantic information. In this paper, we propose a new approach named Top-K Spatio-Topic Query (TKSTQ), which takes semantic information into consideration. We use a topic model to obtain topics of texts and organize index based on topic and location. In this way, the query results can satisfy people’s requirements better. The experimental results on a real dataset validate that our methods can significantly improve the relevance between result and query.



This work is supported by the Natural Science Foundation of China (Grant No. 61532018, 61836007, 61832017).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lianming Zhou
    • 1
  • Xuanhao Chen
    • 1
  • Yan Zhao
    • 2
  • Kai Zheng
    • 1
    Email author
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Computer Science and TechnologySoochow UniversitySuzhouChina

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