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 proceedingConference contributionAcademicpeer-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 languageEnglish
Title of host publicationProceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
PublisherAssociation for Computing Machinery
Pages346-349
Number of pages4
VolumePart F137816
ISBN (Electronic)9781450351911
ISBN (Print)9781450351911
DOIs
Publication statusPublished - 9 Apr 2018
Event33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, France
Duration: 9 Apr 201813 Apr 2018

Conference

Conference33rd Annual ACM Symposium on Applied Computing, SAC 2018
CountryFrance
CityPau
Period9/04/1813/04/18

Keywords

  • Machine learning
  • Personality traits
  • Social contagion

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