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Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions

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Book cover Natural Language Processing and Information Systems (NLDB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10260))

Abstract

Social content generated by users’ interaction in social networks is a knowledge source that may enhance users’ profiles modeling, by providing information on their activities and interests over time. The aim of this article is to propose several original strategies for modeling profiles of social networks’ users, taking into account social information and its temporal evolution. We illustrate our approach on the Twitter network. We distinguish interactive and thematic temporal profiles and we study profiles’ similarities by applying various clustering algorithms, by giving a special attention to overlapping clusters. We compare the different types of profiles obtained and show how they can be relevant for the recommendation of hashtags and users to follow.

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Authors and Affiliations

  1. RIADI Laboratory, National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia

    Abir Gorrab, Ferihane Kboubi & Henda Ben Ghezala

  2. Centre de Recherche En Informatique, University Paris1 Panthéon Sorbonne, Paris, France

    Ali Jaffal & Bénédicte Le Grand

Authors
  1. Abir Gorrab
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  2. Ferihane Kboubi
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  3. Ali Jaffal
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  4. Bénédicte Le Grand
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  5. Henda Ben Ghezala
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Corresponding author

Correspondence to Abir Gorrab .

Editor information

Editors and Affiliations

  1. Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  2. University of Liège , Liège, Belgium

    Ashwin Ittoo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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© 2017 Springer International Publishing AG

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Gorrab, A., Kboubi, F., Jaffal, A., Le Grand, B., Ghezala, H.B. (2017). Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_12

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  • DOI: https://doi.org/10.1007/978-3-319-59569-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59568-9

  • Online ISBN: 978-3-319-59569-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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