A Hybrid Clustered SFLA-PSO algorithm for optimizing the timely and real-time rumor refutations in Online Social Networks

Abstract

Currently, the popularity of various social media platforms has made convenient communications and prompt information spreading in Online Social Networks (OSNs). However, it brings a serious threat to social stability and public safety when information is identified as false, misleading or inappropriate. Therefore, it is valuable that the timely and real-time rumor refutations are respectively studied to promptly and continually block the rumor spreading. Existing works on the rumor refutations are lacking in sufficient decomposition on the trust mechanism and ignore the analysis of continual blocking the rumor spreading. Motivated by these issues, this paper proposes a comprehensive Hybrid Clustered Shuffled Frog-Leaping Algorithm-Particle Swarm Optimization (HCSFLA-PSO) algorithm for promptly and continually blocking the rumor spreading in OSNs. Firstly, by making decomposition of social relations and analyzing degree of intimacy, self-reliability and credibility, a new trust mechanism of rumor refutations and a novel expression of trust degree are proposed. Secondly, a comprehensive HCSFLA-PSO algorithm is proposed by taking advantage of the clustered local search capability of the SFLA and the fast-convergence of the PSO algorithm collaborative for the rumor refutations, which includes CNP-HCSFLA-PSO sub-algorithm for the timely rumor refutation and CP-HCSFLA-PSO sub-algorithm for the real-time rumor refutation during the truth evolution according to different social relations with different trust degrees. Thirdly, a new concept of timeliness of information and a novel energy consumption model are proposed to solve the influence problem of continual updated truths. Finally, numerical simulations and ablation experiments are conducted to verify the effectiveness of our proposed algorithm.