An Adaptive Computational Network Model for Multi-Emotional Social Interaction

Ramona Roller, Suzan Q. Blommestijn, J. Treur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study reported in this paper investigates an adaptive temporal-causal
network-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model's potentials and limitations. The study advances the existing methodology of modelling the social contagion of multiple emotions in a context where also the social network evolves over time.
LanguageEnglish
Title of host publicationProc. of the 6th International Conference on Complex Networks and their Applications
PublisherSpringer Verlag
StatePublished - Nov 2017

Publication series

Name Studies in Computational Intelligence
PublisherSpringer Publishers

Cite this

Roller, R., Blommestijn, S. Q., & Treur, J. (2017). An Adaptive Computational Network Model for Multi-Emotional Social Interaction. In Proc. of the 6th International Conference on Complex Networks and their Applications ( Studies in Computational Intelligence). Springer Verlag.
Roller, Ramona ; Blommestijn, Suzan Q. ; Treur, J./ An Adaptive Computational Network Model for Multi-Emotional Social Interaction. Proc. of the 6th International Conference on Complex Networks and their Applications. Springer Verlag, 2017. ( Studies in Computational Intelligence).
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title = "An Adaptive Computational Network Model for Multi-Emotional Social Interaction",
abstract = "The study reported in this paper investigates an adaptive temporal-causalnetwork-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model's potentials and limitations. The study advances the existing methodology of modelling the social contagion of multiple emotions in a context where also the social network evolves over time.",
author = "Ramona Roller and Blommestijn, {Suzan Q.} and J. Treur",
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Roller, R, Blommestijn, SQ & Treur, J 2017, An Adaptive Computational Network Model for Multi-Emotional Social Interaction. in Proc. of the 6th International Conference on Complex Networks and their Applications. Studies in Computational Intelligence, Springer Verlag.

An Adaptive Computational Network Model for Multi-Emotional Social Interaction. / Roller, Ramona; Blommestijn, Suzan Q.; Treur, J.

Proc. of the 6th International Conference on Complex Networks and their Applications. Springer Verlag, 2017. ( Studies in Computational Intelligence).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An Adaptive Computational Network Model for Multi-Emotional Social Interaction

AU - Roller,Ramona

AU - Blommestijn,Suzan Q.

AU - Treur,J.

PY - 2017/11

Y1 - 2017/11

N2 - The study reported in this paper investigates an adaptive temporal-causalnetwork-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model's potentials and limitations. The study advances the existing methodology of modelling the social contagion of multiple emotions in a context where also the social network evolves over time.

AB - The study reported in this paper investigates an adaptive temporal-causalnetwork-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model's potentials and limitations. The study advances the existing methodology of modelling the social contagion of multiple emotions in a context where also the social network evolves over time.

M3 - Conference contribution

T3 - Studies in Computational Intelligence

BT - Proc. of the 6th International Conference on Complex Networks and their Applications

PB - Springer Verlag

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Roller R, Blommestijn SQ, Treur J. An Adaptive Computational Network Model for Multi-Emotional Social Interaction. In Proc. of the 6th International Conference on Complex Networks and their Applications. Springer Verlag. 2017. ( Studies in Computational Intelligence).