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Quantifying the propagation of distress and mental disorders in social networks

机译:量化社交网络中困扰和精神疾病的传播

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摘要

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak.
机译:人类的异质性导致人们对社会现象的思考和反应不同。意识和同质性促使人们权衡社交多元化网络中的互动,从而影响潜在的传染效应。为了量化异质性对传播动态的影响,我们通过在加权多重网络中引入统计估计量,提出了一种社会传染和意识共同进化的模型。网络个体的多重性可能足以引起传播,从而在经历困扰,精神错乱的易受攻击者中产生影响,这代表着自杀行为的最强预测因子。自杀可能会在精神上有害,因为谈论自杀可能会提供支持或无意中促使自杀。为了揭示重叠意识对无意识人群中自杀意念传播的复杂影响,我们还通过整合不同类型的数据引入了一种数据驱动的方法。我们的建模方法揭示了困扰与精神障碍传播与自杀意念传播之间的关系,阐明了意识在预防自杀的社交网络中的作用。所提出的模型能够量化重叠意识对自杀意念传播的影响,我们的发现表明,它对传染病起着双重作用,可以增强或延迟传染病的爆发。

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