摘要
利用LLR聚类算法和Kleinberg检测算法探究我国大学生抑郁领域研究的热点及前沿发展趋势.检索中国知网收录的大学生抑郁领域相关核心文献,对关键词进行计算分析.结果表明,该领域研究主要围绕行为生活习惯、人际关系、社会网络、人格特征与大学生抑郁的相关性展开,体育活动、睡眠时长、屏幕使用时间等行为活动是该领域的研究前沿.LLR算法结合Kleinberg算法能完成研究热点及前沿发展趋势的检测,为大学生抑郁领域研究提供了新的思路.
Abstract
The study explores the research hot spots and cutting-edge development trend of depression of college students in China with LLR clustering algorithm and Kleinberg detection,searches the key literature related to depression of college students in CNKI,and calculates and analyzes the key words.The results show that the research in this field mainly focuses on the correlation between behaviors,life habits,interpersonal relationships,social networks,personality characteristics and depression of college students.Physical activity,sleep duration,screen use time and other behavioral activities are the research frontiers in this field.Under the combination of LLR algorithm combined with Kleinberg algorithm,we can complete the detection of research hot spots and cutting-edge development trends,and provide new ideas for the study of depression of college students.
基金项目
吉林省教育厅社会科学研究规划项目(JJKH20230946SZ)
NETL
NSTL
维普
万方数据