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社交媒体信息共享特征与中国大学生问题行为之间的关系 根据推算法
Yadong Sun1, Yanjie Shan1, Jiaqiong Xie1
1Faculty of Social Sciences, Chongqing University, Chongqing, People's Republic of China.
Psychology research and behavior management
|July 29, 2024
概括
社交媒体上的推算法影响大学生信息共享和有问题的行为. 受这些算法影响的学生分享更多,并表现出更高水平的内部化和外部化问题.
科学领域:
- 社会科学 社会科学 社会科学
- 心理学 心理学 心理学
- 信息技术 信息技术 信息技术
背景情况:
- 社交媒体平台越来越多地使用推算法.
- 有限的研究存在于这些算法对用户行为和问题结果的影响.
- 大学生是一个主要的人口群体,与社交媒体密切相关.
研究的目的:
- 分析推算法对大学生社交媒体信息共享的影响.
- 调查算法影响的共享与内部化/外部化问题行为之间的关系.
- 解决了解技术在社交媒体相关行为问题中的作用的差距.
主要方法:
- 在34752名中国大学生中进行了在线问卷调查.
- 隐藏的个人资料分析,以确定信息共享模式.
- 博尔克-克罗恩-哈格纳斯 (BCH) 方法回归混合模型分析行为差异.
主要成果:
- 确定了四个不同的信息共享子组:低频,中频和两个高频组 (TikTok,新浪微博).
- 在这些子组中,在内部化和外部化问题行为方面发现了显著的差异.
- 受推算法的影响的大学生表现出更高的信息共享和增加的问题行为.
结论:
- 推算法与大学生信息共享和有问题的行为增加有关.
- 调查结果强调需要考虑社交媒体使用和心理健康中的技术因素.
- 未来的干预措施应侧重于算法意识和优化,以减轻负面影响.
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