Abstract
Objectives
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.
Methods
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.
Results
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.
Conclusions
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.
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Abbreviations
- SIMS:
-
suicide information monitoring system
- URL:
-
Uniform Resource Locator
- SQL:
-
structured query language
- SML:
-
supervised machine learning
References
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.
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.
Zhang, J., and Lin, L., The Moderating Effects of Impulsivity on Chinese Rural Young Suicide. J Clin Psychol, 2013. doi:10.1002/jclp.22039.
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.
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.
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.
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.
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.
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.
Gould, M. S., Suicide and the media. Ann N Y Acad Sci 932:200–21, 2001.
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.
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.
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.
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.
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.
Phillips, D. P., The influence of suggestion on suicide: substantive and theoretical implications of the Werther effect. Am Sociol Rev 39:340–54, 1974.
Stack, S., The effect of the media on suicide: evidence from Japan, 1955–1985. Suicide Life Threat Behav 26(2):132–42, 1996.
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.
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.
Bragazzi, N. L., A Google Trends-based approach for monitoring NSSI. Psychol Res Behav Manag 7:1–8, 2013. doi:10.2147/PRBM.S44084.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Lester, D., Linguistic Analysis of a Blog from a Murder-suicide. Psychol Rep 106:342, 2010.
Ogburn, K. M., Messias, E., and Buckley, P. F., New-age Patient Communications through Social Networks. Gen Hosp Psychiatry 33:200.e1–3, 2011.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Cruz, J. A., and Wishart, D. S., Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Inform 2:59–77, 2007.
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.
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.
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.
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.
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.
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.
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.
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.
Dobson, R., Internet sites may encourage suicide. BMJ 319:337, 1999.
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.
Acknowledgments
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.
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None declared.
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Chen, P., Chai, J., Zhang, L. et al. Development and Application of a Chinese Webpage Suicide Information Mining System (Sims). J Med Syst 38, 88 (2014). https://doi.org/10.1007/s10916-014-0088-z
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DOI: https://doi.org/10.1007/s10916-014-0088-z