Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media

TL;DR

This paper introduces a new approach to predict social media superspreaders by analyzing individual influence and susceptibility, surpassing traditional network centrality metrics, with validation on Twitter and Weibo data.

Contribution

It develops a nonlinear algorithm to quantify influence and susceptibility, providing a novel method for predicting superspreaders beyond existing centrality measures.

Findings

Influence and susceptibility scores predict superspreaders more accurately.

The method outperforms traditional network centrality in prediction.

Insights into the network positions of superspreaders are revealed.

Abstract

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly-connected ``hub" individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals' influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals' estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights on the network position of the superspreaders.