Time-aware domain-based social influence prediction
Bilal Abu-Salih, Kit Yan Chan, Omar S. Al-Kadi, Marwan Al-Tawil, Pornpit Wongthongtham, Tomayess Issa, Heba Saadeh, Malak Al-Hassan, Bushra Bremie, Abdulaziz Albahlal
Abstract
Abstract Online social networks 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, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.