青少年的关系行为和肥胖流行:一项应用社交网络分析和机器学习技术的描述性研究
Pilar Marqués-Sánchez1, María Cristina Martínez-Fernández1, José Alberto Benítez-Andrades2
1Faculty of Health Sciences, SALBIS Research Group, Campus de Ponferrada, Universidad de León, León, Spain.
PloS one
|August 15, 2023
概括
青少年根据饮食和性别等共同属性形成社会子组. 这些发现突显了青少年社交网络中的同性恋,并为肥胖预防策略提供了信息.
科学领域:
- 社交网络分析分析
- 机器学习 机器学习
- 青少年健康 青少年健康
背景情况:
- 了解青少年群体的社会结构对于公共卫生倡议至关重要.
- 饮食和性别是影响青少年健康和社会动态的重要因素.
研究的目的:
- 识别和分析青少年社交网络中的子组.
- 探索子组形成,性别和饮食习惯之间的关系.
- 应用社交网络分析 (SNA) 和人工智能 (AI) 技术来分析群体连接.
主要方法:
- 采用社交网络分析 (SNA),特别是吉尔万-纽曼技术,以确定凝聚力的群体.
- 利用无监督机器学习技术,包括K-means集群,分析人口饮食数据.
- 在2015年3月至12月期间,收集了来自5个教育中心的235名学生的数据.
主要成果:
- 吉尔万-纽曼技术确定了每个教室的不同数量的集群 (2,7和6).
- 鉴定的群体之间在性别和饮食变量方面观察到显著差异.
- 基于饮食的K-平均分组为各自的教室提供了2,3和3个分组.
结论:
- 青少年自然形成子群体,其特点是影响肥胖的共同属性,例如食物质量和性别.
- 同性恋原则解释了青少年社会网络中观察到的子组凝聚力.
- 人工智能和吉尔万-纽曼技术的整合为分析社会子组结构和相似性提供了一个强大的框架.
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