Open AccessResearch on the Identification of College Students’ Mental Health Problems Based on Campus Big Data
Author(s) -
Liang Ge,
Yanyun She,
Jun Yu,
Zhongwei Yuan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/5/052029
Subject(s) - mental health , harm , identification (biology) , anxiety , psychology , depression (economics) , the internet , applied psychology , medical education , clinical psychology , psychiatry , medicine , social psychology , computer science , world wide web , botany , macroeconomics , economics , biology
In recent years, according to the survey, college students have frequent mental health problems, such as anxiety, depression, inferiority, inter-personal sensitivity and other psychological problems, even the idea of suicide. It has a very serious negative impact on the family and society. If mental health problems of college students can be detected early, counselors can pay more attention to these high-risk students. At the same time, the high-risk students can receive therapies as soon as possible, reducing the harm. Therefore, it is crucial to find an effective method for early detection of mental health problems. This paper proposes a new method to detect mental health problems by analyzing students ’behavior on campus. The datasets include Internet access logs, records of entering and leaving dormitories, and records of consumption in canteens. We built classification models for differentiating the mental health problems of students from normal students. The experimental results show that the proposed method may be useful to improve the performance of public mental health services.