Quantifying the propagation of distress and mental disorders in social networks

Scatá, Marialisa; Stefano, Alessandro Di; Corte, Aurelio La; Lió, Píetro

doi:10.1038/s41598-018-23260-2

Cited by 30 publications

(39 citation statements)

References 73 publications

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“…This fact, according to authors such as Scatà and collaborators [44] can prove that said tendency plays a double role in contraction, with this being reinforcing or delaying the noxious ideas that can lead to unhealthy behaviours such as suicide. In addition, these values are justified because the majority of the students belong to different geographic areas [45].…”

Section: Discussionmentioning

confidence: 99%

Social Network Analysis and Resilience in University Students: An Approach from Cohesiveness

Liébana‐Presa

Díaz

Reguera-García

et al. 2018

IJERPH

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The Social Network Analysis offers a view of social phenomena based on interactions. The aim of this study is to compare social reality through the cohesion variable and analyse its relationship with the resilience of university students. This information is useful to work with the students academically and to optimise the properties of the network that have an influence in academic performance. This is a descriptive transversal study with 90 students from the first and third year of the Nursing Degree. Cohesion variables from the support and friendship networks and the level of resilience were gathered. The UCINET programme was used for network analysis and the SPSS programme for statistical analysis. The students’ friendship and support networks show high intra-classroom cohesion although there are no differences between the support networks and friendship or minimal contact networks in both of the courses used for the study. The network cohesion indicators show less cohesion in the third year. No correlations were found between cohesion and resilience. Resilience does not appear to be an attribute related to cohesion or vice versa.

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Section: Discussionmentioning

confidence: 99%

Social Network Analysis and Resilience in University Students: An Approach from Cohesiveness

Liébana‐Presa

Díaz

Reguera-García

et al. 2018

IJERPH

View full text Add to dashboard Cite

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“…The vast majority (90%) of introduced models were designed to work only on simple two layers networks. The only model designed for three layers networks was introduced in [63].…”

Section: ) Number Of Layersmentioning

confidence: 99%

“…There are quite few papers [ [63] are using three layers networks while the rest of them is limited to two layers networks (for details see Table 7). Extending the number of layers in the experiments builds the complexity but at the same time is a must-have to fully understand the mechanisms behind multispread over multilayer networks.…”

Section: ) Number Of Layersmentioning

confidence: 99%

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

2020

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The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward.INDEX TERMS complex networks, information diffusion, multilayer networks, spreading processes

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“…Moreover, we shed light on that awareness measure has not change according to the layers, since awareness once acquired is the same in the different layers in which a user is involved. It can be influenced by the fading of interests on acquiring additional and correlated awareness, or verifying if it is fact-checked or misinformation based [17], [63]. The awareness gap between two interacting nodes in M 1 , impacts on the weights of links as detailed in section III-B1, in conjunction with the homophily value h ij .…”

Section: ) Abstract-basedmentioning

confidence: 99%

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

et al. 2020

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Leveraging social and communication technologies, we can digitally observe that the collective attention typically exhibits a heterogeneous structure. It shows that people's interests are organized in clusters around different topics, but the rising of an extraordinary emergency event, as the coronavirus disease epidemics, channels the people's attention into a more homogenized structure, shifting it as triggered by a non-random collective process. The connectedness of networked individuals, on multiple social levels, impacts on the attention, representing a tuning element of different behavioural outcomes, changing the awareness diffusion enough to produce effects on epidemics spreading. We propose a mathematical framework to model the interplay between the collective attention and the co-evolving processes of awareness diffusion, modelled as a social contagion phenomenon, and epidemic spreading on weighted multiplex networks. Our proposed modelling approach structures a systematically understanding as a social network marker of interdependent collective dynamics through the introduction of the multiplex dimension of both networked individuals and topics, quantifying the role of human-related factors, as homophily, network properties, and heterogeneity. We introduce a data-driven approach by integrating different types of data, digitally traced as user-generated data from Twitter and Google Trends, in response to an extraordinary emergency event as coronavirus disease. Our findings demonstrate how the proposed model allows us to quantify the reaction of the collective attention, proving that it can represent a social predictive marker of the awareness dynamics, unveiling the impact on epidemic spreading, for a timely crisis response planning. Simulations results shed light on the coherence between the data-driven approach and the proposed analytical model.

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Quantifying the propagation of distress and mental disorders in social networks

Cited by 30 publications

References 73 publications

Social Network Analysis and Resilience in University Students: An Approach from Cohesiveness

Social Network Analysis and Resilience in University Students: An Approach from Cohesiveness

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

The Dynamical Interplay of Collective Attention, Awareness and Epidemics Spreading in the Multiplex Social Networks During COVID-19

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