IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v69y2009i3p469-474.html
   My bibliography  Save this article

Connecting the invisible dots: Reaching lesbian, gay, and bisexual adolescents and young adults at risk for suicide through online social networks

Author

Listed:
  • Silenzio, Vincent M.B.
  • Duberstein, Paul R.
  • Tang, Wan
  • Lu, Naiji
  • Tu, Xin
  • Homan, Christopher M.

Abstract

Young lesbian, gay, and bisexual (young LGB) individuals report higher rates of suicide ideation and attempts from their late teens through early twenties. Their high rate of Internet use suggests that online social networks offer a novel opportunity to reach them. This study explores online social networks as a venue for prevention research targeting young LGB. An automated data collection program was used to map the social connections between LGB self-identified individuals between 16 and 24 years old participating in an online social network. We then completed a descriptive analysis of the structural characteristics known to affect diffusion within such networks. Finally, we conducted Monte Carlo simulations of peer-driven diffusion of a hypothetical preventive intervention within the observed network under varying starting conditions. We mapped a network of 100,014 young LGB. The mean age was 20.4 years. The mean nodal degree was 137.5, representing an exponential degree distribution ranging from 1 through 4309. Monte Carlo simulations revealed that a peer-driven preventive intervention ultimately reached final sample sizes of up to 18,409 individuals. The network's structure is consistent with other social networks in terms of the underlying degree distribution. Such networks are typically formed dynamically through a process of preferential attachment. This implies that some individuals could be more important to target to facilitate the diffusion of interventions. However, in terms of determining the success of an intervention targeting this population, our simulation results suggest that varying the number of peers that can be recruited is more important than increasing the number of randomly-selected starting individuals. This has implications for intervention design. Given the potential to access this previously isolated population, this novel approach represents a promising new frontier in suicide prevention and other research areas.

Suggested Citation

  • Silenzio, Vincent M.B. & Duberstein, Paul R. & Tang, Wan & Lu, Naiji & Tu, Xin & Homan, Christopher M., 2009. "Connecting the invisible dots: Reaching lesbian, gay, and bisexual adolescents and young adults at risk for suicide through online social networks," Social Science & Medicine, Elsevier, vol. 69(3), pages 469-474, August.
  • Handle: RePEc:eee:socmed:v:69:y:2009:i:3:p:469-474
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277-9536(09)00325-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Silenzio, V.M.B. & Pena, J.B. & Duberstein, P.R. & Cerel, J. & Knox, K.L., 2007. "Sexual orientation and risk factors for suicidal ideation and suicide attempts among adolescents and young adults," American Journal of Public Health, American Public Health Association, vol. 97(11), pages 2017-2019.
    2. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jongwoo Kim & Richard L. Baskerville & Yi Ding, 2020. "Breaking the Privacy Kill Chain: Protecting Individual and Group Privacy Online," Information Systems Frontiers, Springer, vol. 22(1), pages 171-185, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diego Garlaschelli & Maria I. Loffredo, 2007. "Effects of network topology on wealth distributions," Papers 0711.4710, arXiv.org, revised Jan 2008.
    2. Ya-Chun Gao & Zong-Wen Wei & Bing-Hong Wang, 2013. "Dynamic Evolution Of Financial Network And Its Relation To Economic Crises," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-10.
    3. Guido Caldarelli & Matthieu Cristelli & Andrea Gabrielli & Luciano Pietronero & Antonio Scala & Andrea Tacchella, 2012. "A Network Analysis of Countries’ Export Flows: Firm Grounds for the Building Blocks of the Economy," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-11, October.
    4. Hutzler, S. & Sommer, C. & Richmond, P., 2016. "On the relationship between income, fertility rates and the state of democracy in society," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 9-18.
    5. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    6. Macon, Kevin T. & Mucha, Peter J. & Porter, Mason A., 2012. "Community structure in the United Nations General Assembly," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 343-361.
    7. Barberis, Eduardo & Freddi, Daniela & Giammetti, Raffaele & Polidori, Paolo & Teobaldelli, Désirée & Viganò, Elena, 2020. "Trade Relationships in the European Pork Value Chain: A Network Analysis," Economia agro-alimentare / Food Economy, Italian Society of Agri-food Economics/Società Italiana di Economia Agro-Alimentare (SIEA), vol. 22(1), May.
    8. Marco Bardoscia & Fabio Caccioli & Juan Ignacio Perotti & Gianna Vivaldo & Guido Caldarelli, 2016. "Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.
    9. Rodolfo Baggio & Chris Cooper, 2009. "Knowledge transfer in a tourism destination: the effects of a network structure," The Service Industries Journal, Taylor & Francis Journals, vol. 30(10), pages 1757-1771, November.
    10. Biggiero, Lucio & Angelini, Pier Paolo, 2015. "Hunting scale-free properties in R&D collaboration networks: Self-organization, power-law and policy issues in the European aerospace research area," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 21-43.
    11. Tsekeris, Theodore, 2016. "Interregional trade network analysis for road freight transport in Greece," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 132-148.
    12. F. Daolio & M. Tomassini & K. Bitkov, 2011. "The Swiss board directors network in 2009," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 82(3), pages 349-359, August.
    13. Rong, Rong & Houser, Daniel, 2015. "Growing stars: A laboratory analysis of network formation," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 380-394.
    14. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    15. Shekhtman, Louis M. & Danziger, Michael M. & Havlin, Shlomo, 2016. "Recent advances on failure and recovery in networks of networks," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 28-36.
    16. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2011. "Impact of the Topology of Global Macroeconomic Network on the Spreading of Economic Crises," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    17. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    18. Diego Kozlowski & Viktoriya Semeshenko & Andrea Molinari, 2021. "Latent Dirichlet allocation model for world trade analysis," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    19. Oliver Williams & Charo I Del Genio, 2014. "Degree Correlations in Directed Scale-Free Networks," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-6, October.
    20. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:69:y:2009:i:3:p:469-474. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.