Physical activity contagion and homophily in an adaptive social network model

Marit van Dijk, Jan Treur*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Abstract

Regular physical activity contributes to higher levels of well-being, healthy aging and prevention of several chronic diseases such as depression. To establish or change behaviours concerning physical activity, social contagion may play a role. The aim of this study was to model the contagion of physical activity based on empirical Twitter data and to assess the role of homophily within this contagion. To model the contagion of physical activity, an adaptive temporal-causal network model was designed, and accordingly, the parameters of the model were tuned using empirical data obtained from Twitter. Two variants of the adaptive temporal-causal network model were created, in which one calculated the weights of the connections between the nodes based on follow relations on Twitter, while in the other the connection weights were modulated by the homophily principle. The results indicate that within the considered social network of already active persons homophily does not play an important role in the physical activity behaviour.

Original languageEnglish
Title of host publicationComputational Collective Intelligence
Subtitle of host publication10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I
EditorsNgoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan, Bogdan Trawinski
PublisherSpringer/Verlag
Pages87-98
Number of pages12
Volume1
ISBN (Electronic)9783319984438
ISBN (Print)9783319984421
DOIs
Publication statusPublished - 2018
Event10th International Conference on Computational Collective Intelligence, ICCCI 2018 - Bristol, United Kingdom
Duration: 5 Sept 20187 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11055 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Computational Collective Intelligence, ICCCI 2018
Country/TerritoryUnited Kingdom
CityBristol
Period5/09/187/09/18

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