首页|Beyond network centrality:individual-level behavioral traits for predicting information superspreaders in social media

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

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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 into the network positions of the superspreaders.

complex networkssocial networkssocial mediainformation spreadingsuperspreaders

Fang Zhou、Linyuan Lü、Jianguo Liu、Manuel Sebastian Mariani

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Institute of Fundamental and Frontier Sciences,University of Electronic Science and Technology of China,Chengdu 610054,China

Yangtze Delta Region Institute(Huzhou),University of Electronic Science and Technology of China,Huzhou 313001,China

School of Cyber Science and Technology,University of Science and Technology of China,Hefei 230026,China

Department of Digital Economics,Shanghai University of Finance and Economics,Shanghai 200433,China

URPP Social Networks,Universität Zürich,Zürich 8006,Switzerland

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National Natural Science Foundation of ChinaSTI 2030-Major ProjectsSichuan Province Outstanding Young Scientists FoundationNew Cornerstone Science FoundationNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities in funding'High-Quality Development 23 of Digital EconomySwiss National Science FoundationURPP Social Networks at the University of Zurich

T22937712022ZD02114002023NSFSC191972371150720320032023110139100013-207888

2024

10.1093/nsr/nwae073

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ISSN：
年,卷(期)：2024.11(7)
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