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Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides

Published:24 August 2015Publication History

ABSTRACT

The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.

References

  1. A. G. Billings and R. H. Moos. Coping, stress, and social resources among adults with unipolar depression. Journal of personality and social psychology, 46(4):877, 1984.Google ScholarGoogle Scholar
  2. S. Bird. NLTK: the natural language toolkit. In COLING/ACL Interactive presentation sessions, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research (JMLR), 3:993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Boals and K. Klein. Word use in emotional narratives about failed romantic relationships and subsequent mental health. Journal of Language and Social Psychology, 24(3):252--268, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  5. A. T. Cheng, K. Hawton, T. H. Chen, A. M. Yen, C.-Y. Chen, L.-C. Chen, and P.-R. Teng. The influence of media coverage of a celebrity suicide on subsequent suicide attempts. Journal of Clinical Psychiatry, 68(6):862--866, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. Chester and A. O'Hara. Image, identity and pseudonymity in online discussions. International Journal of Learning, 13(12), 2007.Google ScholarGoogle Scholar
  7. C. Chung and J. W. Pennebaker. The psychological functions of function words. Social Comm., pages 343--359, 2007.Google ScholarGoogle Scholar
  8. G. Coppersmith, M. Dredze, and C. Harman. Quantifying mental health signals in twitter. In ACL Workshop on Computational Linguistics and Clinical Psychology, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  9. G. Coppersmith, C. Harman, and M. Dredze. Measuring post traumatic stress disorder in twitter. In International Conference on Weblogs and Social Media (ICWSM), 2014.Google ScholarGoogle Scholar
  10. M. De Choudhury, S. Counts, E. Horvitz, and A. Hoff. Characterizing and predicting postpartum depression from facebook data. In Computer-Supported Cooperative Work and Social Computing (CSCW), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. De Choudhury and S. De. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In International Conference on Weblogs and Social Media (ICWSM), 2014.Google ScholarGoogle Scholar
  12. M. De Choudhury, M. Gamon, S. Counts, and E. Horvitz. Predicting depression via social media. In International Conference on Weblogs and Social Media (ICWSM), 2013.Google ScholarGoogle Scholar
  13. M. De Choudhury, M. R. Morris, and R. W. White. Seeking and sharing health information online: Comparing search engines and social media. In Human factors in computing systems (CHI), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. K. Dinakar, B. Jones, H. Lieberman, R. Picard, C. Rose, and M. T. R. Reichart. You too?! mixed initiative lda story-matching to help teens in distress. In International Conference on Weblogs and Social Media (ICWSM), 2012.Google ScholarGoogle Scholar
  15. R. M. Fernquist and P. Cutright. Societal integration and age-standardized suicide rates in 21 developed countries, 1955--1989. Social Science Research, 27(2):109--127, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  16. K.-w. Fu and C. Chan. A study of the impact of thirteen celebrity suicides on subsequent suicide rates in south korea from 2005 to 2009. PloS one, 8(1):e53870, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  17. K.-w. Fu and P. S. Yip. Long-term impact of celebrity suicide on suicidal ideation: Results from a population-based study. Journal of Epidemiology and Community Health, 61(6):540--546, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  18. S. A. Golder and M. W. Macy. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051):1878--1881, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  19. M. S. Gould. Suicide and the media. Annals of the New York Academy of Sciences, 932(1):200--224, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  20. S. H. Hemenover. The good, the bad, and the healthy: Impacts of emotional disclosure of trauma on resilient self-concept and psychological distress. Personality and Social Psychology Bulletin, 29(10):1236--1244, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  21. M. Hoffman, F. R. Bach, and D. M. Blei. Online learning for latent dirichlet allocation. In Neural Information Processing Systems (NIPS), 2010.Google ScholarGoogle Scholar
  22. C. M. Homan, N. Lu, X. Tu, M. C. Lytle, and V. Silenzio. Social structure and depression in trevorspace. In Computer-Supported Cooperative Work and Social Computing (CSCW), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. D. J. Houghton and A. N. Joinson. Linguistic markers of secrets and sensitive self-disclosure in twitter. In Hawaii International Conference on System Science (HICSS), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Jamison-Powell, C. Linehan, L. Daley, A. Garbett, and S. Lawson. I can't get no sleep: discussing insomnia on twitter. In Human factors in computing systems (CHI), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. N. Lapidot-Lefler and A. Barak. Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Computers in human behavior, 28(2):434--443, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. A. Long. As if day had rearranged into night: suicidal tendencies in the poetry of anne sexton. Lit Psychol, 39:26--41, 1993.Google ScholarGoogle Scholar
  27. D. D. Luxton, J. D. June, and J. M. Fairall. Social media and suicide: A public health perspective. American Journal of Public Health, 102(S2):S195--S200, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  28. T. R. Lynch, J. Cheavens, J. Q. Morse, and M. Rosenthal. A model predicting suicidal ideation and hopelessness in depressed older adults: The impact of emotion inhibition and affect intensity. Aging & Mental Health, 8(6):486--497,2004.Google ScholarGoogle ScholarCross RefCross Ref
  29. M. A. Moreno, D. A. Christakis, K. G. Egan, L. N. Brockman, and T. Becker. Associations between displayed alcohol references on facebook and problem drinking among college students. Archives of Pediatrics & Adolescent Medicine, 166(2):157--163, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  30. E. L. Murnane and S. Counts. Unraveling abstinence and relapse: smoking cessation reflected in social media. In Human factors in computing systems (CHI), 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. T. Niederkrotenthaler, K.-w. Fu, P. S. Yip, D. Y. Fong, S. Stack, Q. Cheng, and J. Pirkis. Changes in suicide rates following media reports on celebrity suicide: a meta-analysis. Journal of epidemiology and community health, 66(11):1037--1042, 2012.Google ScholarGoogle Scholar
  32. T. Niederkrotenthaler, A. Herberth, and G. Sonneck. The "werther-effect": legend or reality? Neuropsychiatrie: Klinik, Diagnostik, Therapie und Rehabilitation: Organ der Gesellschaft Osterreichischer Nervenarzte und Psychiater, 21(4):284--290, 2006.Google ScholarGoogle Scholar
  33. T. Niederkrotenthaler, B. Till, N. D. Kapusta, M. Voracek, K. Dervic, and G. Sonneck. Copycat effects after media reports on suicide: A population-based ecologic study. Social science & medicine, 69(7):1085--1090, 2009.Google ScholarGoogle Scholar
  34. I. O'Donnell, R. Farmer, and J. Catalan. Suicide notes. The British Journal of Psychiatry, 163(1):45--48, 1993.Google ScholarGoogle ScholarCross RefCross Ref
  35. C. E. Osgood and E. G. Walker. Motivation and language behavior: A content analysis of suicide notes. The Journal of Abnormal and Social Psychology, 59(1):58, 1959.Google ScholarGoogle ScholarCross RefCross Ref
  36. M. Park, D. W. McDonald, and M. Cha. Perception differences between the depressed and non-depressed users in twitter. In International Conference on Weblogs and Social Media (ICWSM), 2013.Google ScholarGoogle Scholar
  37. M. J. Paul and M. Dredze. You are what you tweet: Analyzing twitter for public health. In International Conference on Weblogs and Social Media (ICWSM), 2011.Google ScholarGoogle Scholar
  38. M. J. Paul and M. Dredze. Discovering health topics in social media using topic models. PLoS ONE, 9(8), 2014.Google ScholarGoogle Scholar
  39. M. J. Paul, M. Dredze, and D. Broniatowski. Twitter improves influenza forecasting. PLOS Currents Outbreaks, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  40. J. W. Pennebaker, M. E. Francis, and R. J. Booth. Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71:2001, 2001.Google ScholarGoogle Scholar
  41. J. W. Pennebaker, T. J. Mayne, and M. E. Francis. Linguistic predictors of adaptive bereavement. Journal of personality and social psychology, 72(4):863, 1997.Google ScholarGoogle Scholar
  42. K. Petrie and R. Brook. Sense of coherence, self-esteem, depression and hopelessness as correlates of reattempting suicide. British Journal of Clinical Psychology, 31(3):293--300, 1992.Google ScholarGoogle ScholarCross RefCross Ref
  43. J. E. Pirkis, P. M. Burgess, C. Francis, R. W. Blood, and D. J. Jolley. The relationship between media reporting of suicide and actual suicide in australia. Social science & medicine, 62(11):2874--2886, 2006.Google ScholarGoogle Scholar
  44. D. W. Prezant and R. A. Neimeyer. Cognitive predictors of depression and suicide ideation. Suicide and Life-Threatening Behavior, 18(3):259--264, 1988.Google ScholarGoogle ScholarCross RefCross Ref
  45. K. W. Prier, M. S. Smith, C. Giraud-Carrier, and C. L. Hanson. Identifying health-related topics on twitter. In Social computing, behavioral-cultural modeling and prediction, pages 18--25. Springer, 2011. Google ScholarGoogle ScholarCross RefCross Ref
  46. R.Rěhuřek and P.Sojka. Software Framework for Topic Modelling with Large Corpora. In LREC Workshop on New Challenges for NLP Frameworks, 2010.Google ScholarGoogle Scholar
  47. S. Rude, E.-M. Gortner, and J. Pennebaker. Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8):1121--1133, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  48. T. D. Ruder, G. M. Hatch, G. Ampanozi, M. J. Thali, and N. Fischer. Suicide announcement on facebook. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 32(5):280--282, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  49. A. Sadilek, H. A. Kautz, and V. Silenzio. Modeling spread of disease from social interactions. In International Conference on Weblogs and Social Media (ICWSM), 2012.Google ScholarGoogle Scholar
  50. H. A. Schwartz, J. Eichstaedt, M. L. Kern, G. Park, M. Sap, D. Stillwell, M. Kosinski, and L. Ungar. Towards assessing changes in degree of depression through facebook. In Association for Computational Linguistics (ACL), 2014.Google ScholarGoogle ScholarCross RefCross Ref
  51. E. S. Shneidman. Suicide notes reconsidered. Psychiatry, 36(4):379--394, 1973.Google ScholarGoogle ScholarCross RefCross Ref
  52. E. S. Shneidman and N. L. Farberow. Clues to suicide. Public Health Reports, 71(2):109, 1956.Google ScholarGoogle ScholarCross RefCross Ref
  53. M. A. Silverman and N. P. Will. Sylvia plath and the failure of emotional self-repair through poetry. The Psychoanalytic Quarterly, 1986.Google ScholarGoogle ScholarCross RefCross Ref
  54. M. Sisask and A. Värnik. Media roles in suicide prevention: a systematic review. International journal of environmental research and public health, 9(1):123--138, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  55. J. M. Smyth. Written emotional expression: effect sizes, outcome types, and moderating variables. Journal of consulting and clinical psychology, 66(1):174, 1998.Google ScholarGoogle Scholar
  56. S. Stack. Media coverage as a risk factor in suicide. Journal of epidemiology and community health, 57(4):238--240, 2003.Google ScholarGoogle Scholar
  57. S. W. Stirman and J. W. Pennebaker. Word use in the poetry of suicidal and nonsuicidal poets. Psychosomatic Medicine, 63(4):517--522, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  58. J. Suler. The online disinhibition effect. Cyberpsychology & behavior, 7(3):321--326, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  59. Y. R. Tausczik and J. W. Pennebaker. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24--54, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  60. K. M. Thomas and M. Duke. Depressed writing: Cognitive distortions in the works of depressed and nondepressed poets and writers. Psychology of Aesthetics, Creativity, and the Arts, 1(4):204, 2007.Google ScholarGoogle Scholar
  61. I. M. Wasserman. Imitation and suicide: A reexamination of the werther effect. American sociological review, pages 427--436, 1984.Google ScholarGoogle Scholar
  62. H.-H. Won, W. Myung, G.-Y. Song, W.-H. Lee, J.-W. Kim, B. J. Carroll, and D. K. Kim. Predicting national suicide numbers with social media data. PloS one, 8(4):e61809, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  63. P. S. Yip, K.-W. Fu, K. C. Yang, B. Y. Ip, C. L. Chan, E. Y. Chen, D. T. Lee, F. Y. Law, and K. Hawton. The effects of a celebrity suicide on suicide rates in hong kong. Journal of affective disorders, 93(1):245--252, 2006.Google ScholarGoogle Scholar

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      cover image ACM Conferences
      HT '15: Proceedings of the 26th ACM Conference on Hypertext & Social Media
      August 2015
      360 pages
      ISBN:9781450333955
      DOI:10.1145/2700171

      Copyright © 2015 ACM

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      • Published: 24 August 2015

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