Detecting the risk of bullying victimization among adolescents: A large-scale machine learning approach

There is an increasing interest in using machine learning methods to identify risk factors for problematic behaviors. The current study tested and compared six machine learning algorithms: Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Light Gradient B...

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Bibliographic Details

Published in:	Computers in human behavior Vol. 147; p. 107817
Main Authors:	Yan, Wei, Yuan, Yidan, Yang, Menghao, Zhang, Peng, Peng, Kaiping
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.10.2023
Subjects:	Adolescents Cyberbullying victimization Machine learning Risk factors Traditional bullying victimization Traditional bullying victimization Adolescents Risk factors Cyberbullying victimization Machine learning
ISSN:	0747-5632, 1873-7692
Online Access:	Get full text
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