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This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of…
This paper aims to present social trust as a variable of influence by demonstrating the possibilities of trusted social nodes to improve influential capability and rate of successfully influenced social nodes within a social networking environment.
This research will be conducted using simulated experiments. The base algorithm in research uses genetics algorithm diffusion model (GADM) where it carries out social influence calculations within a social networking environment. The GADM algorithm will be enhanced by integrating trust values into its influential calculations. The experiment simulates a virtual social network based on a social networking site architecture from the data set used to conduct experiments on the enhanced GADM and observe their influence capabilities.
The presence of social trust can effectively increase the rate of successfully influenced social nodes by factorizing trust value of one source node and acceptance rate of another recipient node into its probabilistic equation, hence increasing the final acceptance probability.
This research focused exclusively on conceptual mathematical models and technical aspects so far; comprehensive user study, extensive performance and scalability testing is left for future work.
Two key contributions of this paper are the calculation of social trust via content integrity and the application of social trust in social influential diffusion algorithms. Two models will be designed, implemented and evaluated on the application of social trust via trusted social nodes and domain-specified (of specific interest groups) trusted social nodes.