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Communication Dans Un Congrès Année : 2017

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

Résumé

Social content generated by users' interactions 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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Dates et versions

hal-01549588 , version 1 (28-06-2017)

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Abir Gorrab, Ferihane Kboubi, Bénédicte Le Grand, Henda Ben Ghezala, Ali Jaffal. Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions. 22nd International Conference on Applications of Natural Language to Information Systems (NLDB 2017), Jun 2017, Liège, Belgium. pp.124-130, ⟨10.1007/978-3-319-59569-6_12⟩. ⟨hal-01549588⟩

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