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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media
Organization Unit
  • Fang Zhou
  • Linyuan Lu
  • Jianguo Liu
  • Manuel Mariani
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title National Science Review
Publisher Oxford University Press
Geographical Reach international
ISSN 2095-5138
Volume 11
Number 7
Page Range nwae073
Date 2024
Abstract Text 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.
Free access at DOI
Digital Object Identifier 10.1093/nsr/nwae073
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Keywords Multidisciplinary