Drimux Dynamic Rumor Influence Minimization with User Experience in Social Networks

Abstract

With the taking off improvement of huge scale online social networks, online data sharing is getting to be omnipresent regular. Different data is engendering through online social networks including both the positive and negative. In this paper, we center around the negative data issues, for example, the online rumors. Rumor blocking is a difficult issue in expansive scale social networks. Vindictive rumors could cause mayhem in the public arena and henceforth should be obstructed as quickly as time permits in the wake of being recognized. In this paper, we propose a model of dynamic rumor impact minimization with client encounter (DRIMUX). We will probably limit the impact of the rumor (i.e., the quantity of clients that have acknowledged and sent the rumor) by blocking a specific subset of hubs.