Reliable user profile analytics and discovery on social networks

Hazimeh, Hussein (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Mugellini, Elena (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland) ; Abou Khaled, Omar (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

In this paper, we introduce heterogeneous methods to analyze and discover user profiles on Online Social Networks (OSNs).We are the first to investigate such methods to profile users on multiple OSNs (Facebook, Twitter, Google+, etc.). In addition, we perform reliable analytics, i.e., users in the datasets are identical. Deeply speaking, if we have a dataset of n number of user profiles on Facebook, we do not analyze n different profiles on corresponding OSN. However, we first perform a user Profile Matching (PM) task from a seed dataset (Facebook for instance) and then match these profiles inside this dataset to their corresponding profiles on other OSNs, then we start our User Profile Analysis and Discovery task (UPAD). We show that our UPAD methods uncover very interesting facts about OSN users.


Mots-clés:
Type de conférence:
full paper
Faculté:
Ingénierie et Architecture
Ecole:
HEIA-FR
Institut:
HumanTech - Technology for Human Wellbeing Institute
Adresse bibliogr.:
Penang, Malaysia, 19-21 February 2019
Date:
2019-02
Penang, Malaysia
19-21 February 2019
Pagination:
5 p.
Publié dans:
Proceedings of ACM 8th International Conference on Software and Computer Applications (ICSCA 2019), 19-21 February 2019, Penang, Malaysia
Numérotation (vol. no.):
pp. 496-500
DOI:
ISBN:
9781450365734
Le document apparaît dans:

Note: The status of this file is: restricted


 Notice créée le 2020-01-14, modifiée le 2020-10-27

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