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USING PAGERANK AND SOCIAL NETWORK ANALYSIS TO SPECIFY MENTAL HEALTH FACTORS

Published online by Cambridge University Press:  27 July 2021

Arsineh Boodaghian Asl ,
Jayanth Raghothama ,
Adam Darwich  and
Sebastiaan Meijer
Arsineh Boodaghian Asl*
Affiliation:
KTH Royal Institute of Technology
Jayanth Raghothama
Affiliation:
KTH Royal Institute of Technology
Adam Darwich
Affiliation:
KTH Royal Institute of Technology
Sebastiaan Meijer
Affiliation:
KTH Royal Institute of Technology
*
Boodaghian Asl, Arsineh, KTH Royal Institute of Technology, Sweden, arsineh@kth.se

Abstract

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Various factors influence mental well-being, and span individual, social and familial levels. These factors are connected in many ways, forming a complex web of factors and providing pathways for developing programs to improve well-being and for further research. These factors can be studied individually using traditional methods and mapped together to be analyzed holistically from a complex system perspective. This study provides a novel approach using PageRank and social network analysis to understand such maps. The motives are: (1) to realize the most influential factors in such complex networks, (2) to understand factors that influence variations from different network aspects. A previously developed map for children's mental well-being was adopted to evaluate the approach. To achieve our motives, we have developed an approach using PageRank and Social Network Analysis. The results indicate that regardless of the network scale, two key factors called "Quantity and Quality of Relationships" and "Advocacy" can influence children's mental well-being significantly. Moreover, the divergence analysis reveals that one factor, "Recognition/Value Placed on well-being at School" causes a wide range of diffusion throughout the system.

Keywords

Process modellingComplexityComputational design methodsSocial Network AnalysisWell-being
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3379 - 3388
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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