Temporal Aspects of Tree Hole Data

Authors

  • Zengzhen Du Hubei University of Chinese Medicine, Wuhan, Hubei, China
  • Dan Xie Hubei University of Chinese Medicine, Wuhan, Hubei, China
  • Min Hu Hubei University of Chinese Medicine, Wuhan, Hubei, China

DOI:

https://doi.org/10.2991/jaims.d.210604.001

Keywords:

Tree hole, Suicide assistance, Temporal aspects

Abstract

At present, adolescent suicide becomes a serious social problem. Many young people express suicidal thoughts through online social media. Weibo is a famous social media platform for real-time information sharing in China. When a Weibo user committed suicide, many other users continued to post information on this Weibo. Such a space is often called a “tree hole.” By analyzing the temporal aspects of tree hole data, we can understand the behavioral characteristics of suicide attempters and provide more valuable information for suicide assistance. This paper will introduce the analysis of temporal characteristics of tree hole data and guide suicide assistance through suicide monitoring and early warning based on these time characteristics.

References

X.D. Wang, An analysis of the causes of contemporary college students’ suicide, Asia-Pac. Educ. 10 (2015), 246–201.

A. Abboute, Y. Boudjeriou, G. Entringer, et al., Mining Twitter for suicide prevention, in International Conference on Applications of Natural Language to Data Bases/information Systems, Montpellier, France, 2014, pp. 250–253.

H. Xu, J. Li, A study on the current situation of suicide on internet, J. Psychiatry. 28 (2015), 153–155.

L. Wang, C.Y. Li, “QQ suicide group” in network security, China Pub. Secur. Acad. Ed. 46 (2013), 92–94.

A. Ikunaga, S.R. Nath, K.A. Skinner, Internet suicide in Japan: a qualitative content analysis of a suicide bulletin board, Transcult. Psychiatry. 50 (2013), 280–302.

S.S. Lv, On anonymity effect of tree hole propagation, Lit. Educ. 12 (2012), 33–34.

Z.S. Huang, Y.W. Min, F. Lin, et al., Time characteristics of suicide information in social media, China Digital Med. 14 (2019), 7–10.

P. Chen, Y.X. Qian, Z.S. Huang, et al., Negative emotional characteristics of Weibo “Tree Hole” users, Chin. Ment. Health J. 34 (2020), 437–444.

J.Q. Gong, S.F. Lin, Study on spatial characteristics of data of patients with depression in “Tree Hole” of microblog, China Digital Med. 15 (2020), 70–74.

W. Tian, T.S. Zhu, Deep learning model for suicidal identification of Chinese microblogs, J. Univ. Chin. Acad. Sci. 35 (2018), 131–136.

S. Achinta, G. Satyajit, S. Debahit, et al., A study on rice production using time series analysis of Assam, India, the Materials Today: Proceedings is a journal, 2020, pp. 2214–7853.

H.M. Zhang, Time series analysis of Chinese agricultural gross domestic product, World Sci. Res. J. 6 (2020), 33–38.

G. Patricia, S. Jakob, S. Tina, et al., How does learners’ behavior attract preservice teachers’ attention during teaching?, Teach. Teach. Educ. 97 (2021), 103213.

M. Nordentoft, Q. Ping, K. Helweg-Larsen, et al., Time-trends in method-specific suicide rates compared with the availability of specific compounds, The Danish experience, Nord. J. Psychiatry. 60 (2006), 97–106.

Published

2021-05-05

How to Cite

1.
Du Z, Xie D, Hu M. Temporal Aspects of Tree Hole Data. JAIMS [Internet]. 2021 May 5 [cited 2024 May 18];2(1-2):55-61. Available from: http://ojs.ais.cn/jaims/article/view/65