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

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Published:22 December 2021Publication 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)Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  5. Jieba Segmentation web address https://github.com/fxsjy/jieba.Google ScholarGoogle Scholar
  6. Salton G, Buckley C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management, 1988, 24(5):513--523.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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)Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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)Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  11. Wang C. Depression and suicidal behavior among college students[D]. Colorado: University of Denver, 2013.Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  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)Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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)Google ScholarGoogle Scholar

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            cover image ACM Other conferences
            ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences
            October 2021
            593 pages
            ISBN:9781450395588
            DOI:10.1145/3500931

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            Publication History

            • Published: 22 December 2021

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