Journal of Medical Systems

, 38:88 | Cite as

Development and Application of a Chinese Webpage Suicide Information Mining System (Sims)

  • Penglai Chen
  • Jing Chai
  • Lu Zhang
  • Debin Wang
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement



This study aims at designing and piloting a convenient Chinese webpage suicide information mining system (SIMS) to help search and filter required data from the internet and discover potential features and trends of suicide.


SIMS utilizes Microsoft Visual Studio2008, SQL2008 and C# as development tools. It collects webpage data via popular search engines; cleans the data using trained models plus minimum manual help; translates the cleaned texts into quantitative data through models and supervised fuzzy recognition; analyzes and visualizes related variables by self-programmed algorithms.


The SIMS developed comprises such functions as suicide news and blogs collection, data filtering, cleaning, extraction and translation, data analysis and presentation. SIMS-mediated mining of one-year webpage revealed that: peak months and hours of web-reported suicide events were June-July and 10–11 am respectively, and the lowest months and hours, September-October and 1–7 am; suicide reports came mostly from Soho, Tecent, Sina etc.; male suicide victims over counted female victims in most sub-regions but southwest China; homes, public places and rented houses were the top three places to commit suicide; poisoning, cutting vein and jumping from building were the most commonly used methods to commit suicide; love disputes, family disputes and mental diseases were the leading causes.


SIMS provides a preliminary and supplementary means for monitoring and understanding suicide. It proposes useful aspects as well as tools for analyzing the features and trends of suicide using data derived from Chinese webpages. Yet given the intrinsic “dual nature” of internet-based suicide information and the tremendous difficulties experienced by ourselves and other researchers, there is still a long way to go for us to expand, refine and evaluate the system.


Suicide News Blogs Data mining Support system 



suicide information monitoring system


Uniform Resource Locator


structured query language


supervised machine learning



This paper was co-supported by the Natural Science Foundation of China (grant number 81172201) and Anhui Provincial Fund for Elite Youth (grant number 2011SQRL060). Penglai Chen and Jing Chai contributed equally to this manuscript.

Conflict interest

None declared.


  1. 1.
    Fleischmann, A., and Saxena, S., Suicide prevention in the WHO mental health gap action programme (mhGAP). Crisis 34:295–6, 2013. doi: 10.1027/0227-5910/a000214.Google Scholar
  2. 2.
    Keshavan, M. S., Shenoy, S., and Li, H., Suicide in Asian Countries. Asian J Psychiatr 6:355, 2013. doi: 10.1016/j.ajp.2013.08.063.CrossRefGoogle Scholar
  3. 3.
    Zhang, J., and Lin, L., The Moderating Effects of Impulsivity on Chinese Rural Young Suicide. J Clin Psychol, 2013. doi: 10.1002/jclp.22039.Google Scholar
  4. 4.
    Simon, M., Chang, E. S., Zeng, P., et al., Prevalence of Suicidal Ideation, Attempts, and Completed Suicide Rate in Chinese Aging Populations. A Systematic Review. Arch Gerontol Geriatr 57:250–256, 2013. doi: 10.1016/j.archger.2013.05.006.CrossRefGoogle Scholar
  5. 5.
    Lu, J., Xiao, Y., Xu, X., et al., The Suicide Rates in the Yunnan Province, a Multi-ethnic Province in Southwestern China. Int J Psychiatry Med 45:83–96, 2013.CrossRefGoogle Scholar
  6. 6.
    Zhao, J., Zhao, J., Xiao, R., et al., Suicide exposure and its modulatory effects on relations between life events and suicide risk in Chinese college students. Nan Fang Yi Ke Da Xue Xue Bao 33:1111–6, 2013.Google Scholar
  7. 7.
    Chang, S. S., Page, A., and Gunnell, D., Internet searches for a specific suicide method follow its high-profile media coverage. Am J Psychiatry 168:855–7, 2011. doi: 10.1176/appi.ajp.2011.11020284.CrossRefGoogle Scholar
  8. 8.
    Ju Ji, N., Young Lee, W., Seok Noh, M., and Yip, P. S., The impact of indiscriminate media coverage of a celebrity suicide on a society with a high suicide rate: epidemiological findings on copycat suicides from South Korea. J Affect Disord 156:56–61, 2014. doi: 10.1016/j.jad.2013.11.015.CrossRefGoogle Scholar
  9. 9.
    Hegerl, U., Koburger, N., Rummel-Kluge, C., Gravert, C., Walden, M., and Mergl, R., One followed by many?-Long-term effects of a celebrity suicide on the number of suicidal acts on the German railway net. J Affect Disord 146:39–44, 2013. doi: 10.1016/j.jad.2012.08.032.CrossRefGoogle Scholar
  10. 10.
    Gould, M. S., Suicide and the media. Ann N Y Acad Sci 932:200–21, 2001.CrossRefGoogle Scholar
  11. 11.
    Nakamura, M., Yasunaga, H., Toda, A. A., et al., The impact of media reports on the 2008 outbreak of hydrogen sulfide suicides in Japan. Int J Psychiatry Med 44:133–40, 2012.CrossRefGoogle Scholar
  12. 12.
    Dunlop, S. M., More, E., and Romer, D., Where do youth learn about suicides on the Internet, and what influence does this have on suicidal ideation? J Child Psychol Psychiatry 52:1073–80, 2011. doi: 10.1111/j.1469-7610.2011.02416.x. Epub 2011 Jun 10.CrossRefGoogle Scholar
  13. 13.
    Sisask, M., and Värnik, A., Media roles in suicide prevention: a systematic review. Int J Environ Res Public Health 9:123–38, 2012. doi: 10.3390/ijerph9010123.CrossRefGoogle Scholar
  14. 14.
    Niederkrotenthaler, T., Voracek, M., Herberth, A., et al., Role of media reports in completed and prevented suicide: Werther v. Papageno effects. Br J Psychiatry 197:234–43, 2010. doi: 10.1192/bjp.bp.109.074633.CrossRefGoogle Scholar
  15. 15.
    Niederkrotenthaler, T., and Sonneck, G., Assessing the impact of media guidelines for reporting on suicides in Austria: Interrupted time series analysis. Aust N Z J Psychiatry 41:419–428, 2007.CrossRefGoogle Scholar
  16. 16.
    Phillips, D. P., The influence of suggestion on suicide: substantive and theoretical implications of the Werther effect. Am Sociol Rev 39:340–54, 1974.CrossRefGoogle Scholar
  17. 17.
    Stack, S., The effect of the media on suicide: evidence from Japan, 1955–1985. Suicide Life Threat Behav 26(2):132–42, 1996.Google Scholar
  18. 18.
    Yip, P. S., Fu, K. W., Yang, K. C., et al., The effects of a celebrity suicide on suicide rates in Hong Kong. J Affect Disord 93:245–52, 2006.CrossRefGoogle Scholar
  19. 19.
    Cheng, A. T., Hawton, K., Chen, T. H., et al., The influence of media coverage of a celebrity suicide on subsequent suicide attempts. J Clin Psychiatry 68:862–6, 2007.CrossRefGoogle Scholar
  20. 20.
    Bragazzi, N. L., A Google Trends-based approach for monitoring NSSI. Psychol Res Behav Manag 7:1–8, 2013. doi: 10.2147/PRBM.S44084.CrossRefGoogle Scholar
  21. 21.
    Whitlock, J. L., Powers, J. L., and Eckenrode, J., The virtual cutting edge: the internet and adolescent self-injury. Dev Psychol 42:407–17, 2006.CrossRefGoogle Scholar
  22. 22.
    Birbal, R., Maharajh, H. D., Birbal, R., et al., Cybersuicide and the adolescent population: challenges of the future? Int J Adolesc Med Health 21:151–159, 2009.Google Scholar
  23. 23.
    Kemp, C. G., and Collings, S. C., Hyperlinked suicide: assessing the prominence and accessibility of suicide websites. Crisis 32:143–51, 2011. doi: 10.1027/0227-5910/a000068.Google Scholar
  24. 24.
    Gunn, J. F., and Lester, D., Using google searches on the internet to monitor suicidal behavior. J Affect Disord 148:411–2, 2013. doi: 10.1016/j.jad.2012.11.004.CrossRefGoogle Scholar
  25. 25.
    Sueki, H., Does the volume of Internet searches using suicide-related search terms influence the suicide death rate: data from 2004 to 2009 in Japan. Psychiatry Clin Neurosci 65:392–394, 2011. doi: 10.1111/j.1440-1819.2011.02216.x.CrossRefGoogle Scholar
  26. 26.
    Hagihara, A., Miyazaki, S., and Abe, T., Internet suicide searches and the incidence of suicide in young people in Japan. Eur Arch Psychiatry Clin Neurosci 262:39–46, 2012. doi: 10.1007/s00406-011-0212-8.CrossRefGoogle Scholar
  27. 27.
    Yang, A. C., Tsai, S. J., Huang, N. E., et al., Association of Internet Search Trends with Suicide Death in Taipei City, Taiwan, 2004–2009. J Affect Disord 132:179–184, 2011. doi: 10.1016/j.jad.2011.01.019.CrossRefGoogle Scholar
  28. 28.
    Recupero, P. R., Harms, S. E., and Noble, J. M., Googling suicide: surfing for suicide information on the internet. J Clin Psychitary 69:878–888, 2008.CrossRefGoogle Scholar
  29. 29.
    Eysenbach, G., Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. Am J Prev Med 40:S154–S158, 2011. doi: 10.1016/j.amepre.2011.02.006.CrossRefGoogle Scholar
  30. 30.
    Eysenbach, G., Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res 11:e11, 2009. doi: 10.2196/jmir.1157.CrossRefGoogle Scholar
  31. 31.
    Schwartz, K. D., Lutfiyya, Z. M., et al., In Pain Waiting to Die’: Everyday Understandings of Suffering. Palliat Support Care 10:27–36, 2012. doi: 10.1017/S1478951511000551.CrossRefGoogle Scholar
  32. 32.
    Cash, S. J., Thelwall, M., Peck, S. N., Ferrell, J. Z., and Bridge, J. A., Adolescent suicide statements on MySpace. Cyberpsychol Behav Soc Netw 16:166–74, 2013. doi: 10.1089/cyber.2012.0098.CrossRefGoogle Scholar
  33. 33.
    Ruder, T. D., Hatch, G. M., Ampanozi, G., Thali, M. J., and Fischer, N., Suicide announcement on Facebook. Crisis 32:280–2, 2011. doi: 10.1027/0227-5910/a000086.Google Scholar
  34. 34.
    Lester, D., Linguistic Analysis of a Blog from a Murder-suicide. Psychol Rep 106:342, 2010.CrossRefGoogle Scholar
  35. 35.
    Ogburn, K. M., Messias, E., and Buckley, P. F., New-age Patient Communications through Social Networks. Gen Hosp Psychiatry 33:200.e1–3, 2011.CrossRefGoogle Scholar
  36. 36.
    Bragazzi, N. L., From P0 to P6 medicine, a model of highly participatory, narrative, interactive, and “augmented” medicine: some considerations on Salvatore Iaconesi’s clinical story. Patient Prefer Adherence 7:353–9, 2013. doi: 10.2147/PPA.S38578.CrossRefGoogle Scholar
  37. 37.
    Tatsis, V. A., Tjortjis, C., and Tzirakis, P., Evaluating Data Mining Algorithms Using Molecular Dynamics Trajectories. Int J Data Min Bioinform 8:169–87, 2013.CrossRefGoogle Scholar
  38. 38.
    Gurulingappa, H., Toldo, L., Rajput, A. M., et al., Automatic Detection of Adverse Events to Predict Drug Label Changes Using Text and Data Mining Techniques. Pharmacoepidemiol Drug Saf 22:1189–94, 2013. doi: 10.1002/pds.3493.CrossRefGoogle Scholar
  39. 39.
    Wang, Y. F., Chang, M. Y., Chiang, R. D., et al., Mining medical data: a case study of endometriosis. J Med Syst 37:9899, 2013. doi: 10.1007/s10916-012-9899-y.CrossRefGoogle Scholar
  40. 40.
    Shen, C. P., Jigjidsuren, C., and Dorjgochoo, S., A data-mining framework for transnational healthcare system. J Med Syst 36:2565–75, 2012. doi: 10.1007/s10916-011-9729-7.CrossRefGoogle Scholar
  41. 41.
    Vest, J. R., Jasperson ’S. How are health professionals using health information exchange systems? Measuring usage for evaluation and system improvement. J Med Syst 36:3195–204, 2012. doi: 10.1007/s10916-011-9810-2.CrossRefGoogle Scholar
  42. 42.
    McCarthy, M. J., Internet monitoring of suicide risk in the population. J Affect Disord 122:277–279, 2010. doi: 10.1016/j.jad.2009.08.015.CrossRefGoogle Scholar
  43. 43.
    Song, T. M., Song, J., An, J. Y., Hayman, L. L., and Woo, J. M., Psychological and social factors affecting Internet searches on suicide in Korea: a big data analysis of Google search trends. Yonsei Med J 55:254–63, 2014. doi: 10.3349/ymj.2014.55.1.254.CrossRefGoogle Scholar
  44. 44.
    Lai, M. H., Maniam, T., Chan, L. F., and Ravindran, A. V., Caught in the web: a review of web-based suicide prevention. J Med Internet Res 16:e30, 2014. doi: 10.2196/jmir.2973.CrossRefGoogle Scholar
  45. 45.
    Katase, H., Kanazawa, M., Inokoshi, M., et al., Face Simulation System for Complete Dentures by Applying Rapid Prototyping. J Prosthet Dent 109:353–360, 2013. doi: 10.1016/S0022-3913(13)60316-9.CrossRefGoogle Scholar
  46. 46.
    Cruz, J. A., and Wishart, D. S., Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Inform 2:59–77, 2007.Google Scholar
  47. 47.
    Stormo, G. D., Schneider, T. D., Gold, L., et al., Use of the “Perceptron” algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res 10:2997–3011, 1982.CrossRefGoogle Scholar
  48. 48.
    DeLisle, S., Kim, B., Deepak, J., et al., Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy. PLoS ONE 8:e70944, 2013. doi: 10.1371/journal.pone.0070944.CrossRefGoogle Scholar
  49. 49.
    Moratilla, J. M., Alonso-Calvo, R., Molina-Vaquero, G., et al., A Data Model Based on Semantically Enhanced HL7 RIM for Sharing Patient Data of Breast Cancer Clinical Trials. Stud Health Technol Inform 192:971, 2013.Google Scholar
  50. 50.
    Ougrin, D., Banarsee, R., Dunn-Toroosian, V., et al., Suicide Survey in a London Borough: Primary Care and Public Health Perspectives. J Public Health (Oxf) 33:385–391, 2011. doi: 10.1093/pubmed/fdq094.CrossRefGoogle Scholar
  51. 51.
    Niederkrotenthaler, T., Till, B., Herberth, A., et al., The Gap Between Suicide Characteristics in the Print Media and in the Population. Eur J Public Health 19:361–364, 2009. doi: 10.1093/eurpub/ckp034.CrossRefGoogle Scholar
  52. 52.
    van Ballegooijen, W., Riper, H., Klein, B., et al., An Internet-Based Guided Self-Help Intervention for Panic Symptoms: Randomized Controlled Trial. J Med Internet Res 15:e154, 2013. doi: 10.2196/jmir.2362.CrossRefGoogle Scholar
  53. 53.
    Ryhänen, A. M., Rankinen, S., Tulus, K., et al., Internet based patient pathway as an educational tool for breast cancer patients. Int J Med Inform 81:270–278, 2012. doi: 10.1016/j.ijmedinf.2012.01.010.CrossRefGoogle Scholar
  54. 54.
    Black, E., Light, J., Paradise Black, N., et al., Online Social Network Use by Health Care Providers in a High Traffic Patient Care Environment. J Med Internet Res 15:e94, 2013. doi: 10.2196/jmir.2421.CrossRefGoogle Scholar
  55. 55.
    Dobson, R., Internet sites may encourage suicide. BMJ 319:337, 1999.CrossRefGoogle Scholar
  56. 56.
    Manning, J., and Vandeusen, K., Suicide prevention in the dot com era: technological aspects of a university suicide prevention program. J Am Coll Health 59:431–3, 2011. doi: 10.1080/07448480903540507.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Health Service ManagementAnhui Medical UniversityHefeiChina

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