Extraction of Characteristics of Time in “Tree Hole” Data

Authors

Xiaomin Jing^{1, *,} , Shaofu Lin², Zhisheng Huang³

¹The Information Department, Beijing University of Technology, Beijing, China

²Beijing Institute of Smart City, Beijing University of Technology, Beijing, China

³Department of Computer Science, Vrije University Amsterdam, Amsterdam, The Netherlands

^*Corresponding author. E-mail: jingxiaomin5@163.com

DOI

10.2991/jaims.d.201209.001How to use a DOI?

Keywords

Depression; Microblog; Tree hole; Knowledge graph; Time characteristics; Rescue

Abstract

Statistics show that 15 percent of depressed people died by suicide, and more than 50 percent of depressed people are thinking about suicide. Worldwide, depression has become the second leading cause of death among people aged 15–29. This paper focus on the “tree hole” message data on microblog, and conducts data visualization research from different granularity, such as quarter, month, and analyses activity of message during holiday based on the knowledge graph, so as to obtain the national time distribution characteristics of the potential risk of mental health for the reference of social institutions’ monitoring and rescue and government departments’ decision-making. According to the time distribution rule of “tree hole” data, the relatively high occurrence time and possible reasons for depression and suicide are found, so that manpower could be reasonably deployed for effective prevention and rescue.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaomin Jing
AU  - Shaofu Lin
AU  - Zhisheng Huang
PY  - 2020
DA  - 2020/12/14
TI  - Extraction of Characteristics of Time in “Tree Hole” Data
JO  - Journal of Artificial Intelligence for Medical Sciences
SP  - 43
EP  - 48
VL  - 1
IS  - 3-4
SN  - 2666-1470
UR  - https://doi.org/10.2991/jaims.d.201209.001
DO  - 10.2991/jaims.d.201209.001
ID  - Jing2020
ER  -