A Distributed Method for Negative Content Spread Minimization on Social Networks

Currently, social networks have emerged as significant platforms for individuals to share personal information and social content. However, it is important to recognize that social networks have both positive and negative aspects. To effectively address the dissemination of negative social content such as rumors and misinformation, it is crucial to implement strategies that involve immediate blocking of associated links. This paper introduces a Negative Content Spread Minimization (NCSM) problem, which aims to minimize the spread of negative content by removing a set of edges from the network. We begin by demonstrating the NP-hardness of the NCSM problem through reduction from the Knapsack Problem. Furthermore, we establish that the objective function is not submodular under the Independent Cascade model. To address, we employ a distributed method which includes community partition and influential edges selection. The advantage of this approach is to reduce computational overhead by selecting key edges in parallel in each community. To evaluate proposed algorithm, we conduct experiments using real-world datasets and compare them against existing methods. The experimental results demonstrate that our method outperforms state-of-the-art algorithms.