Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Home
Unveiling Roots Of Chinese Adolescent Cyberbullying Through Explainable Machine Learning Approach.

Home
Unveiling Roots Of Chinese Adolescent Cyberbullying Through Explainable Machine Learning Approach.

Related Concept Videos

Bullying

Bullying

A modern form of aggression is bullying. As you learn in your study of child development, socializing and playing with other children is beneficial for children’s psychological development. However, as you may have experienced as a child, not all play behavior has positive outcomes. Some children are aggressive and want to play roughly. Other children are selfish and do not want to share toys. One form of negative social interactions among children that has become a national concern is bullying.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Cross-Lagged Study of Helicopter Parenting, Parent-Child Conflict, and Adolescents' Short Video Addiction: The Moderating Role of Trait Autonomy.

Behavioral sciences (Basel, Switzerland)·2026

Same author

Justifying the Unjustifiable: Unpacking the Temporal Associations Between Moral Disengagement and Adolescent-to-Parent Violence.

Journal of adolescence·2026

Same author

Enhancing the Diagnostic Performance of Early Lung Cancer by Integrating Multiplex Nucleic Acid Aptamer Detection With Conventional Tumor Markers: A Prospective Observational Study.

American journal of clinical oncology·2026

Same author

The Mechanism and Design Principles of Serious Games in Enhancing Adolescents' Internet Adaptability.

JMIR serious games·2026

Same author

Characterization and screening of a novel octapeptide from protein hydrolysates of <i>Crassostrea gigas</i> and its immunomodulatory effects on RAW264.7 cells.

Food chemistry: X·2026

Same author

Study on the mining stress field distribution law beneath isolated coal pillar in close coal seam and reasonable location of the roadway.

Scientific reports·2026

Same journal

Adolescents and Adults Use Different Facial Movements and Vocal Cues to Express Emotions.

Developmental science·2026

Same journal

Decoding Preschool Social Dynamics: Automated Tracking of Spatial and Temporal Patterns to Investigate Social Interactions and Relationships in Peer Groups.

Developmental science·2026

Same journal

Early Life Stress Affects Human Decision Making by Increasing Expectations of Volatility.

Developmental science·2026

Same journal

The Impact of Intergroup Collaboration on In-Group Bias Between Rohingya Refugee and Bangladeshi Host Community Children.

Developmental science·2026

Same journal

Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning.

Developmental science·2026

Same journal

Infants' Domain-General Responses to Expectancy Violations.

Developmental science·2026

See all related articles

Related Experiment Videos

Unveiling Roots of Chinese Adolescent Cyberbullying Through Explainable Machine Learning Approach.

Wanghao Dong^1,2, Yinghui Huang³, Xin Zhao⁴

¹Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan, Hubei, China.

Developmental Science

|June 2, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Childhood psychological abuse and adverse peer interactions significantly predict adolescent cyberbullying. Explainable machine learning (ML) identifies key factors across individual, family, and online contexts for effective prevention strategies.

Keywords:

cyberbullying perpetration ecosystem theory explainable machine learning predictive modeling

Related Experiment Videos

Area of Science:

Psychology
Computer Science
Public Health

Background:

Cyberbullying is a major threat to adolescent well-being.
Limited understanding of multilevel determinants hinders effective cyberbullying prevention.
Ecological systems theory provides a framework for examining environmental influences.

Purpose of the Study:

To apply explainable machine learning (ML) to identify multilevel determinants of cyberbullying perpetration in adolescents.
To analyze factors across individual, family, peer, class, school, and online contexts.
To inform multi-tiered intervention strategies for adolescent cyberbullying prevention.

Main Methods:

Utilized questionnaire data from 2286 adolescents (ages 11-16).
Employed explainable ML algorithms (Random Forest, XGBoost) for predictive modeling.
Conducted model-based importance analyses to rank predictor significance.

Main Results:

Random Forest and XGBoost achieved high predictive accuracies (87.35% and 85.95%).
Childhood Psychological Abuse, Adverse Peer Interactions, and Cyberbullying Victimization were top predictors.
Family, Individual, and Cyber contexts significantly contributed to model importance.

Conclusions:

Explainable ML effectively synthesizes complex questionnaire data for cyberbullying research.
Childhood psychological abuse is a critical target for intervention.
Findings support the development of multi-tiered, ecosystem-informed prevention strategies against adolescent cyberbullying.