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Analysis of emotional characteristics of Weibo "tree hole" users with different suicide risk

Published: 22 December 2021 Publication History

Abstract

Background Suicide is a global public health and mental health problem. With the rapid development of internet technology, more and more people tend to express their suicidal tendencies and suicidal intentions online. The difference of emotional characteristics between high and low risk of suicide messages should be analyzed to help identify suicide risk and provide early intervention. Methods The "tree hole" intelligent robot captures message data, then randomly selects the same number of high and low suicide risk messages manually, and the high frequency keywords of high and low suicide risk messages are obtained by word segmentation and a TF-IDF algorithm. The keywords are analyzed by Gephi software, and the emotion dictionary provided by Boson is used to judge the emotional tendency of high and low suicide risk users. Results The emotional score of high suicide risk messages was -3.511 ~ 2.514, averaging (-0.225±0.405), while the total score of low suicide risk messages was -4.547 ~ 3.403, averaging (-0.121±0.628). Low suicide risk messages mainly focused on negative emotions, interpersonal relationships and social support, while high suicide risk messages mainly centered on invited suicide, means, locations and time of suicide. Conclusion There are differences in emotional characteristics between high and low suicide risk messages. The higher the suicide risk, the more obvious the negative tendency of the users' emotions. More attention is needed to the greater potential for suicide among this group of users and psychological support and interventions should be included.

References

[1]
Gao X, Jin Y, Wang Y, et al. Analysis on suicide mortality and self-inflicted injury/suicide hospital cases in China from 2006 to 2016[J]. Chinese Journal of Preventive Medicine, 2019, 53 (9): 885--890. (in Chinese)
[2]
Geng S N. On the influence of the application of network "tree hole" on the harmony and stability of colleges and universities------Taking "tree hole" Weibo as an example [J]. Ideological & Theoretical Education. 2013: 76--78, 82. (in Chinese)
[3]
Huang Z S, Hu Q, Gu G J, et al. Web-based Intelligent Agents for Suicide Monitoring and Early Warning[J]. China Digital Medicine, 2019, 14 (3): 3--6. (in Chinese)
[4]
Huang Z S, Min Y W, Lin F, et al. Time Characteristics of Suicide Information in Social Media [J]. China Digital Medicine, 2019, 14(03): 7--10. (in Chinese)
[5]
Jieba Segmentation web address https://github.com/fxsjy/jieba.
[6]
Salton G, Buckley C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5):513--523.
[7]
Chen P, Qian Y X, Huang Z S, et al. Negative emotional characteristics of Weibo "Tree Hole" users[J/OL]. Chinese Mental Health Journal, 2020(05):437--444. (in Chinese)
[8]
Wang Z, Yu G, Tian X, et al. A Study of users with suicidal ideation on Sina Weibo[J]. Telemed J E Health, 2018, 24(9):702--709.
[9]
Li Y T, Huang Y J, Lin X Q, et al. Analysis of college students' suicide cases and the reasons behind them [J]. Ability and Wisdom, 2018, (22): 144--145. (in Chinese)
[10]
Nock M K, Kessler R C. Prevalence of and risk factors for suicide attempts versus suicide gestures: analysis of the national comorbidity survey[J]. J Abnorm Psychol, 2006, 115 (3): 616--623.
[11]
Wang C. Depression and suicidal behavior among college students[D]. Colorado: University of Denver, 2013.
[12]
Wang J Y, He Y L. Progress in mental health literacy [J]. Journal of Neuroscience and Mental Health, 2013, 13 (1): 98--101. (in Chinese)
[13]
Cong E C, Wu Y, Cai Y Y, et al. Association of suicidal ideation with family environment and psychological resilience in adolescents [J]. Chinese Journal of Contemporary Pediatrics, 2019, 21 (5): 479--484. (in Chinese)
[14]
Wang W, Wu X M. The role of protective factors in suicide protection[J]. Chinese Journal of Health Education, 2013, 29(12):1110--1112. (in Chinese)
[15]
Blum R W, Halcon L, Beuhring T, et a1. Adolescent health in the Caribbean: Risk and protective factors[J], Am J Public Health, 2003, 93(3):456--460.
[16]
Masten A S, Coatsworth J D-The development of competence in favorable and unfavorable environments: Lessons from research on successful children[J].Am Psychol, 1998, 53(2): 205--220.
[17]
Wang D B, Lai G X, Xia C Y. A study on the risk factors of suicidal behaviors in patients with major depression [J]. Chinese Journal of Nervous and Mental Diseases, 2002, 28 (2): 88--89. (in Chinese)

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ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
October 2021
593 pages
ISBN:9781450395588
DOI:10.1145/3500931
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2021

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Author Tags

  1. Artificial intelligence
  2. Emotional characteristics analysis
  3. Suicide risk
  4. Weibo "tree hole"

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ISAIMS 2021

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Overall Acceptance Rate 53 of 112 submissions, 47%

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