Activity Minimization Of Misinformation Influence In Online Social Networks

Abstract

In recent years, online social media has flourished, and a large amount of information has spread through social platforms, changing the way in which people access information. The authenticity of information content is weakened, and all kinds of misinformation rely on social media to spread rapidly. Network space governance and providing a trusted network environment are of critical significance. In this article, we study a novel problem called activity minimization of misinformation influence (AMMI) problem that blocks a node set from the network such that the total amount of misinformation interaction between nodes (TAMIN) is minimized. That is to say, the AMMI problem is to select K nodes from a given social network G to block so that the TAMIN is the smallest. We prove that the objective function is neither sub modular nor super modular and propose a heuristic greedy algorithm (HGA) to select top K nodes for removal. Furthermore, in order to evaluate our proposed method, extensive experiments have been carried out on three real-world networks. The experimental results demonstrate that our proposed method outperforms comparison approaches The proliferation of online social media platforms in recent years has fundamentally reshaped how individuals access and consume information. While these platforms offer unprecedented opportunities for connectivity and information sharing, they have also become fertile ground for the rapid dissemination of misinformation. This erosion of information authenticity poses a significant challenge to network space governance and underscores the critical need for establishing a trusted online environment. In response to this growing concern, this study delves into a novel problem termed the Activity Minimization of Misinformation Influence (AMMI) problem. The core objective of the AMMI problem is to strategically identify and block a specific set of K nodes within a given social network G in such a way that the total amount of misinformation interaction between the remaining nodes (TAMIN) is minimized. Essentially, we aim to select K influential nodes whose removal would most effectively curtail the spread and interaction of misinformation across the network.neutralize key nodes that facilitate misinformation flow represents a crucial step towards effective network governance and the preservation of information integrity in the digital age.