Inferring Implicit Topical Interests on Twitter

  • Fattane Zarrinkalam
  • Hossein Fani
  • Ebrahim Bagheri
  • Mohsen Kahani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)


Inferring user interests from their activities in the social network space has been an emerging research topic in the recent years. While much work is done towards detecting explicit interests of the users from their social posts, less work is dedicated to identifying implicit interests, which are also very important for building an accurate user model. In this paper, a graph based link prediction schema is proposed to infer implicit interests of the users towards emerging topics on Twitter. The underlying graph of our proposed work uses three types of information: user’s followerships, user’s explicit interests towards the topics, and the relatedness of the topics. To investigate the impact of each type of information on the accuracy of inferring user implicit interests, different variants of the underlying representation model are investigated along with several link prediction strategies in order to infer implicit interests. Our experimental results demonstrate that using topics relatedness information, especially when determined through semantic similarity measures, has considerable impact on improving the accuracy of user implicit interest prediction, compared to when followership information is only used.


Implicit interest Twitter Topic relatedness Collaborative filtering 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fattane Zarrinkalam
    • 1
    • 2
  • Hossein Fani
    • 1
    • 3
  • Ebrahim Bagheri
    • 1
  • Mohsen Kahani
    • 2
  1. 1.Laboratory for Systems, Software and Semantics (LS3)Ryerson UniversityTorontoCanada
  2. 2.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran
  3. 3.Faculty of Computer ScienceUniversity of New BrunswickNew BrunswickCanada

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