User role identification based on social behavior and networking analysis for information dissemination

Xiaokang Zhou, Bo Wu, Qun Jin

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Nowadays, along with the high development of emerging computational paradigms, more and more populations have been involved into the social revolution across various intelligent systems, which results in dynamic user connections associated with a variety of social behaviors. The associated users with different properties, who can be regarded as one kind of information resources, have become increasingly important, especially in social knowledge creation and human intelligence utilization processes. In this study, we concentrate on user role identification based on their social connections and influential behaviors, in order to facilitate information sharing and propagation in social networking environments. Following the construction of a dynamic user networking model, we propose a network-aware method to identify four kinds of special users, who may play an important role in information delivery among a group of users, or knowledge sharing between pairs of users. A set of attributes and measures is proposed and calculated to identify and represent these users based on the analysis of their influence-related social behaviors and dynamic connections. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of the proposed method using Twitter data. Analysis results show the effectiveness of our approach in identifying the distinct features of four kinds of users from the user networking model. Comparison experiments indicate that the proposed identification method outperforms two other related works. Finally, a questionnaire-based evaluation demonstrates the accuracy and efficiency of the proposed method in terms of finding these users in a real social networking environment.

    Original languageEnglish
    JournalFuture Generation Computer Systems
    DOIs
    Publication statusAccepted/In press - 2016 Aug 23

    Fingerprint

    Information dissemination
    Intelligent systems
    Identification (control systems)
    Experiments

    Keywords

    • Information dissemination
    • Social behavior
    • Social networking analysis
    • User identification
    • User modeling

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Networks and Communications

    Cite this

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    abstract = "Nowadays, along with the high development of emerging computational paradigms, more and more populations have been involved into the social revolution across various intelligent systems, which results in dynamic user connections associated with a variety of social behaviors. The associated users with different properties, who can be regarded as one kind of information resources, have become increasingly important, especially in social knowledge creation and human intelligence utilization processes. In this study, we concentrate on user role identification based on their social connections and influential behaviors, in order to facilitate information sharing and propagation in social networking environments. Following the construction of a dynamic user networking model, we propose a network-aware method to identify four kinds of special users, who may play an important role in information delivery among a group of users, or knowledge sharing between pairs of users. A set of attributes and measures is proposed and calculated to identify and represent these users based on the analysis of their influence-related social behaviors and dynamic connections. Experiments and evaluations are conducted to demonstrate the practicability and usefulness of the proposed method using Twitter data. Analysis results show the effectiveness of our approach in identifying the distinct features of four kinds of users from the user networking model. Comparison experiments indicate that the proposed identification method outperforms two other related works. Finally, a questionnaire-based evaluation demonstrates the accuracy and efficiency of the proposed method in terms of finding these users in a real social networking environment.",
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