Applying machine learning algorithms for deriving personality traits in social network

Eric F.M. Araújo; Bojan Simoski; Michel Klein

doi:10.1145/3167132.3167377

Applying machine learning algorithms for deriving personality traits in social network

Eric F.M. Araújo, Bojan Simoski, Michel Klein

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of better models. We propose and evaluate a new methodology for finding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. Then we try to fine tune the model using machine learning algorithms. The fine tuning of questions from an intake questionnaire can be very useful in validating a model. The accuracy delivered by machine learning pure algorithms is shown to be better, but the inclusion of data related to people's traits is beneficial in defining their characteristics.

Original language	English
Title of host publication	Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Publisher	Association for Computing Machinery
Pages	346-349
Number of pages	4
Volume	Part F137816
ISBN (Electronic)	9781450351911
ISBN (Print)	9781450351911
DOIs	https://doi.org/10.1145/3167132.3167377
Publication status	Published - 9 Apr 2018
Event	33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France Duration: 9 Apr 2018 → 13 Apr 2018

Conference

Conference	33rd Annual ACM Symposium on Applied Computing, SAC 2018
Country/Territory	France
City	Pau
Period	9/04/18 → 13/04/18

Funding

Acknowledgments: E.F.M. Araújo’s funding is provided by the Science without Borders Program (CAPES, reference 13538-13-6).

Funders	Funder number
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior	13538-13-6

Keywords

Machine learning
Personality traits
Social contagion

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/3167132.3167377

Applying machine learning algorithms for deriving personality traits in social networkFinal published version, 1.07 MBLicence: Article 25fa Dutch Copyright Act

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abstract = "Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of better models. We propose and evaluate a new methodology for finding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. Then we try to fine tune the model using machine learning algorithms. The fine tuning of questions from an intake questionnaire can be very useful in validating a model. The accuracy delivered by machine learning pure algorithms is shown to be better, but the inclusion of data related to people's traits is beneficial in defining their characteristics.",

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booktitle = "Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018",

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note = "33rd Annual ACM Symposium on Applied Computing, SAC 2018 ; Conference date: 09-04-2018 Through 13-04-2018",

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Araújo, EFM, Simoski, B & Klein, M 2018, Applying machine learning algorithms for deriving personality traits in social network. in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018. vol. Part F137816, Association for Computing Machinery, pp. 346-349, 33rd Annual ACM Symposium on Applied Computing, SAC 2018, Pau, France, 9/04/18. https://doi.org/10.1145/3167132.3167377

Applying machine learning algorithms for deriving personality traits in social network. / Araújo, Eric F.M.; Simoski, Bojan; Klein, Michel.
Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018. Vol. Part F137816 Association for Computing Machinery, 2018. p. 346-349.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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N2 - Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of better models. We propose and evaluate a new methodology for finding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. Then we try to fine tune the model using machine learning algorithms. The fine tuning of questions from an intake questionnaire can be very useful in validating a model. The accuracy delivered by machine learning pure algorithms is shown to be better, but the inclusion of data related to people's traits is beneficial in defining their characteristics.

AB - Social and cognitive sciences' knowledge about social behavior and social networks combined with the new computational machine learning techniques can facilitate the creation of better models. We propose and evaluate a new methodology for finding personality traits of young adults involved in a network using hyper optimization algorithms. We used a social contagion model for the spread of behavior (measured by the physical activity level) among the participants. A part of the Big-5 questionnaire was used to gather information about people regarding their traits of openness and expressiveness. Then we try to fine tune the model using machine learning algorithms. The fine tuning of questions from an intake questionnaire can be very useful in validating a model. The accuracy delivered by machine learning pure algorithms is shown to be better, but the inclusion of data related to people's traits is beneficial in defining their characteristics.

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Applying machine learning algorithms for deriving personality traits in social network

Abstract

Conference

Funding

Keywords

UN SDGs

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