Using machine learning algorithms and techniques for defining the impact of affective temperament types, content search and activities on the internet on the development of problematic internet use in adolescents’ population

Datos Bibliográficos

ID22073646
AutoresJelena Jović (0000-0002-7623-0553, autor de correspondencia), Aleksandar Čorac (0000-0002-1689-8076), Aleksandar Stanimirović (0000-0001-8772-4930, University of Nis), Mina Nikolić (0009-0009-9356-5935, University of Nis), Marko Stojanović (0000-0001-7933-8386, University of Nis), Zoran Bukumirić (0000-0002-7609-4504, University of Belgrade), Dragana Ignjatović Ristić (0000-0002-2814-3105, University of Kragujevac)
Año2024
Volumen12
Páginas1326178-1326178
Fecha de publicación2024-05-17
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Public Health (JOURNAL)
Identificadores de la revistaISSN: 2296-2565 • E-ISSN: 2296-2565
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1326178
PMID38827621
OpenAlexW4397021763
IdiomaEN
Citas recibidas1
Referencias citadas69

Background: By using algorithms and Machine Learning - ML techniques, the aim of this research was to determine the impact of the following factors on the development of Problematic Internet Use (PIU): sociodemographic factors, the intensity of using the Internet, different contents accessed on the Internet by adolescents, adolescents' online activities, life habits and different affective temperament types. Methods: Sample included 2,113 adolescents. The following instruments were used: questionnaire about: socio-demographic characteristics, intensity of the Internet use, content categories and online activities on the Internet; Facebook (FB) usage and life habits; The Internet Use Disorder Scale (IUDS). Based on their scores on the scale, subjects were divided into two groups - with or without PIU; Temperament Evaluation of Memphis, Pisa, Paris, and San Diego scale for adolescents (A-TEMPS-A). Results: Various ML classification models on our data set were trained. Binary classification models were created (class-label attribute was PIU value). Models hyperparameters were optimized using grid search method and models were validated using k-fold cross-validation technique. Random forest was the model with the best overall results and the time spent on FB and the cyclothymic temperament were variables of highest importance for these model. We also applied the ML techniques Lasso and ElasticNet. The three most important variables for the development of PIU with both techniques were: cyclothymic temperament, the longer use of the Internet and the desire to use the Internet more than at present time. Group of variables having a protective effect (regarding the prevention of the development of PIU) was found with both techniques. The three most important were: achievement, search for contents related to art and culture and hyperthymic temperament. Next, 34 important variables that explain 0.76% of variance were detected using the genetic algorithms. Finally, the binary classification model (with or without PIU) with the best characteristics was trained using artificial neural network. Conclusion: Variables related to the temporal determinants of Internet usage, cyclothymic temperament, the desire for increased Internet usage, anxious and irritable temperament, on line gaming, pornography, and some variables related to FB usage consistently appear as important variables for the development of PIU

Geography · Machine learning · Personality · Population · Temperament · The Internet · World Wide Web · Applied Psychology · Computer Science · Digital Mental Health Interventions · Impact of Technology on Adolescents · Medicine · Mental Health via Writing · Psychology · Social Psychology · Artificial Intelligence