Mitigating Misinformation Spreading in Social Networks Via Edge Blocking

TL;DR

This paper addresses the challenge of reducing misinformation spread in social networks by proposing a community-based edge blocking algorithm that outperforms existing methods.

Contribution

It introduces a novel community-based approach for edge blocking to minimize misinformation spread, validated through experiments on real-world networks.

Findings

The problem of edge blocking for misinformation mitigation is computationally hard.

The proposed community-based algorithm significantly outperforms prior centrality-based methods.

Experimental results demonstrate the effectiveness of the new approach on real social network data.

Abstract

The wide adoption of social media platforms has brought about numerous benefits for communication and information sharing. However, it has also led to the rapid spread of misinformation, causing significant harm to individuals, communities, and society at large. Consequently, there has been a growing interest in devising efficient and effective strategies to contain the spread of misinformation. One popular countermeasure is blocking edges in the underlying network. We model the spread of misinformation using the classical Independent Cascade model and study the problem of minimizing the spread by blocking a given number of edges. We prove that this problem is computationally hard, but we propose an intuitive community-based algorithm, which aims to detect well-connected communities in the network and disconnect the inter-community edges. Our experiments on various real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures.