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Employing decision trees to predict cyberbullying victimization among Chinese adolescents and identify subgroups and their shared characteristics

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成果类型:
期刊论文
作者:
Youzhi Song;Yuan Tian;Cuiying Fan;Quan Zheng;Lu Huang;...
通讯作者:
Zongkui Zhou
作者机构:
Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China
School of Psychology, Central China Normal University, Wuhan, China
[Lu Huang] School of Marxism, Wuhan Business University, Wuhan, China
[Youzhi Song; Yuan Tian; Cuiying Fan; Quan Zheng; Zongkui Zhou] Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China<&wdkj&>School of Psychology, Central China Normal University, Wuhan, China
通讯机构:
[Zongkui Zhou] K
Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China<&wdkj&>School of Psychology, Central China Normal University, Wuhan, China
语种:
英文
关键词:
Cyberbullying;Traditional bullying;Depression;Decision tree;Machine learning
期刊:
Current Psychology
ISSN:
1046-1310
年:
2024
页码:
1-14
基金类别:
This work was financially supported by the Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality at Beijing Normal University in China [grant number 2021-04-003-BZPK01], the Program of National Natural Science Funds of China [grant number 61907020], and the Fundamental Research Funds for the Central Universities [grant number CCNU22JC002].
机构署名:
本校为第一且通讯机构
院系归属:
心理学院
摘要:
Early research has revealed distinct subgroups of cyberbullying victims. However, due to the limitations of traditional statistical methods, the characterization of features in the subgroups has been relatively limited, making it challenging to gain a relatively comprehensive understanding of different subgroup members. Decision trees and machine learning techniques offer notable advantages in addressing such issues. The primary aim of this study is to develop a high-performing classifier based on self-reported data from 814 middle school stude...

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