A Novel Approach to Limit the Spread of Wrong Information in Social Networks

Abstract

In this effort, we study the idea of competing campaigns in a social network. By demonstrating the spread of effect in the presence of competing campaigns, we provide necessary tools for applications such as emergency response where the goal is to limit the spread of misinformation. We revisited the problematic of effect limitation where a bad campaign twitches spreading from a certain node in the grid and use the notion of limiting campaigns to counteract the misinformation. The problem can be summarized as identifying a subset of folks that need to be convinced to adopt the competing (or \decent") movement so as to minimalize the number of people that adopt the \bad" campaign at the end of both propagation processes. We demonstration that this optimization problematic is NP-hard and deliver estimate assurances for an avaricious answer for various meanings of this problem by proving that they are submodular. Though the greedy algorithm is a polynomial time algorithm, for today's big scale social networks even this answer is computation-ally actual expensive. Consequently, we study the presentation of the degree importance experiential as well as other heuristics that have insinuations on our exact problem. The experiments on a number of close-knit regional networks obtained from the Facebook social network show that in most belongings inexpensive heuristics do in fact compare well with the greedy approach.