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Related Concept Videos

Bullying02:04

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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.
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Antisocial Personality Disorder01:24

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Related Experiment Video

Updated: May 13, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Machine learning-based risk warning for adolescents' prosocial behavior.

Xuan Li1

  • 1Department of Psychology, Fudan University, Shanghai, China.

Applied Psychology. Health and Well-Being
|May 12, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning identified key predictors of prosocial behavior in adolescents. Perceived relative deprivation and negative attachment significantly influence positive social behavior and adjustment.

Keywords:
adolescentattachmentmachine learningprosocial behaviorrelative deprivation

Related Experiment Videos

Last Updated: May 13, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Adolescent Psychology
  • Machine Learning in Social Sciences
  • Behavioral Science

Background:

  • Prosocial behavior is crucial for adolescent mental health and performance.
  • Ecological systems and social cognitive theories guide understanding of behavior.
  • Identifying risk factors for prosocial behavior is essential for intervention.

Purpose of the Study:

  • To utilize machine learning to identify risk factors for adolescent prosocial behavior.
  • To compare the efficacy of six machine learning algorithms in predicting prosocial behavior.
  • To pinpoint key individual, community, family, school, and social factors influencing prosocial behavior.

Main Methods:

  • Employed six machine learning algorithms: Logistic Regression, Naive Bayes, Decision Tree, Random Forest, KNN, and LightGBM.
  • Analyzed 55 potential risk factors for prosocial behavior in a sample of 8,364 adolescents (ages 13-15).
  • Utilized multi-stage cluster random sampling in Zhejiang province, China.

Main Results:

  • Logistic Regression and LightGBM demonstrated superior performance compared to other algorithms.
  • Individual perceived relative deprivation and negative attachment type were identified as significant predictors.
  • These factors are crucial for predicting prosocial behavior and aiding social adjustment.

Conclusions:

  • Machine learning effectively identifies critical factors influencing adolescent prosocial behavior.
  • Perceived relative deprivation and attachment styles are key targets for interventions.
  • Findings support early identification and support for adolescents struggling with social well-being and potential issues like internet addiction.