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Suicide Ideation of Individuals in Online Social Networks

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  • Naoki Masuda
  • Issei Kurahashi
  • Hiroko Onari

Abstract

: Suicide explains the largest number of death tolls among Japanese adolescents in their twenties and thirties. Suicide is also a major cause of death for adolescents in many other countries. Although social isolation has been implicated to influence the tendency to suicidal behavior, the impact of social isolation on suicide in the context of explicit social networks of individuals is scarcely explored. To address this question, we examined a large data set obtained from a social networking service dominant in Japan. The social network is composed of a set of friendship ties between pairs of users created by mutual endorsement. We carried out the logistic regression to identify users’ characteristics, both related and unrelated to social networks, which contribute to suicide ideation. We defined suicide ideation of a user as the membership to at least one active user-defined community related to suicide. We found that the number of communities to which a user belongs to, the intransitivity (i.e., paucity of triangles including the user), and the fraction of suicidal neighbors in the social network, contributed the most to suicide ideation in this order. Other characteristics including the age and gender contributed little to suicide ideation. We also found qualitatively the same results for depressive symptoms.

Suggested Citation

  • Naoki Masuda & Issei Kurahashi & Hiroko Onari, 2013. "Suicide Ideation of Individuals in Online Social Networks," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.
  • Handle: RePEc:plo:pone00:0062262
    DOI: 10.1371/journal.pone.0062262
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    References listed on IDEAS

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    3. Bearman, P.S. & Moody, J., 2004. "Suicide and Friendships among American Adolescents," American Journal of Public Health, American Public Health Association, vol. 94(1), pages 89-95.
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    1. Matsuzawa, Ryo & Tanimoto, Jun & Fukuda, Eriko, 2017. "Properties of a new small-world network with spatially biased random shortcuts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 408-415.
    2. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    3. Theyazn H. H. Aldhyani & Saleh Nagi Alsubari & Ali Saleh Alshebami & Hasan Alkahtani & Zeyad A. T. Ahmed, 2022. "Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models," IJERPH, MDPI, vol. 19(19), pages 1-16, October.

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