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    An Approach for Time-aware Domain-based Social Influence Prediction

    Access Status
    In process
    Authors
    Abu Salih, Bilal
    Chan, Kit Yan
    Al-Kadi, Omar
    Al-Tawil, Marwan
    Wongthongtham, Pornpit
    Issa, Tomayess
    Saadeh, Heba
    Al-Hassan, Malak
    Bremie, Bushra
    Albahlal, Abdulaziz
    Date
    2020
    Type
    Journal Article
    
    Metadata
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    Citation
    Abu-Salih, B. and Chan, K.Y. and Al-Kadi, O. and Al-Tawil, M. and Wongthongtham, P. and Issa, T. and Saadeh, H. et al. 2020. An Approach for Time-aware Domain-based Social Influence Prediction.
    Faculty
    Faculty of Business and Law
    Faculty of Business and Law
    School
    School of Management and Marketing
    School of Management and Marketing
    URI
    http://hdl.handle.net/20.500.11937/96242
    Collection
    • Curtin Research Publications
    Abstract

    Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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