id: 06026039
dt: a
an: 06026039
au: Alkouz, Akram; De Luca, Ernesto William; Albayrak, Sahin
ti: Latent semantic social graph model for expert discovery in facebook.
so: Eichler, Gerald (ed.) et al., 11th international conference on innovative
    internet community services (I$^{2}$CS 2011). Bonn: Gesellschaft für
    Informatik e.V. (GI) (ISBN 978-3-88579-280-2). Lect. Notes Inform. 186,
    128-138 (2011).
py: 2011
pu: Bonn: Gesellschaft für Informatik e.V. (GI)
la: EN
cc: 
ut: 
ci: 
li: ioport:LNI/Proceedings/Proceedings186/128.pdf
ab: Summary: Expert finding systems employ social networks analysis and natural
    language processing to identify candidate experts in organization or
    enterprise datasets based on a user’s profile, her documents, and her
    interaction with other users. Expert discovery in public social
    networks such as Facebook faces the challenges of matching users to a
    wide range of expertise areas, because of the diverse human interests.
    In this paper we analyze the social graph and the user’s interactions
    in the form of posts and group memberships to model user interests and
    fields of expertise. The proposed model reflects expertise and
    interests of users based on experimental analysis of the explicit and
    implicit social data in Online Social Networks (OSNs). It employs
    social networks analysis, text mining, text classification, and
    semantic text similarity techniques to analyze and discover the latent
    semantic social graph model that can express user’s expertise. The
    proposed model also considers the semantic similarity between user’s
    posts and his groups, Influence of friendship on group’s membership,
    and Influence of friendship on user’s posts. Experiments on the
    Facebook data show significant validity of the proposed model.
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