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.

中文翻译：

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超越网络中心性：预测社交媒体中信息超级传播者的个人行为特征

了解个体在大规模信息传播中的异质作用对于管理在线行为及其潜在的线下后果至关重要。为此，来自不同研究领域的大多数现有研究都集中在高度联系的“中心”个体所扮演的不成比例的角色。然而，我们在这里证明，通过同时考虑两个个人层面的行为特征：影响力和敏感性，可以最好地理解和预测在线社交媒体中的信息超级传播者。具体来说，我们推导了一种基于非线性网络的算法来量化多个传播事件数据中个体的影响力和敏感性。通过将该算法应用于来自 Twitter 和微博的大规模数据，我们证明了个人的估计影响力和易感性分数能够预测超越网络中心性的未来超级传播者，并揭示关于超级传播者网络地位的新见解。