在青少年中使用机器学习方法探索欺凌行为的预测因素
Huiling Zhou1, Qubo Zheng2, Huaibin Jiang3
1Department of Psychology, Shanghai Normal University, China.
Journal of interpersonal violence
|May 19, 2025
概括
机器学习确定了青少年欺凌的关键因素. 自制成为一个重要的保护因素,而道德疏远是欺凌行为的主要风险因素.
科学领域:
- 青少年心理学 青少年心理学
- 机器学习应用程序 机器学习应用程序
- 行为科学是一种行为科学.
背景情况:
- 欺凌行为是青少年的一个重要问题.
- 了解个人和环境风险/保护因素对于干预至关重要.
研究的目的:
- 采用机器学习来识别青少年欺凌行为的风险和保护因素.
- 为了比较六个机器学习算法的检测这些因素的有效性.
主要方法:
- 利用了777名青少年 (年龄11-16岁) 的数据集.
- 收集的多维数据包括个人因素 (道德脱离,规范信念,神经病,自我控制) 和环境因素 (亲子关系,同行关系,学校联系,暴力媒体曝光).
- 测试并比较了六种机器学习算法:逻辑回归,随机森林,梯度增强决策树,XGBoost,LightGBM和堆叠.
主要成果:
- 随机森林展示了高回忆 (0.9394),F1得分 (0.8516) 和AUC (0.8043) 的最佳性能.
- 吉尼的重要性和夏普利添加式解释 (SHAP) 都将自我控制确定为最重要的保护因素.
- 伦理脱离被确定为欺凌行为最有影响力的风险因素.
结论:
- 机器学习模型,特别是随机森林,有效地检测欺凌风险和保护因素.
- 自制和道德脱离是影响欺凌行为的关键个体因素.
- 这些发现为制定有针对性的干预措施以预防青少年欺凌提供了科学基础.
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