Abstract
The suicide tendency is increasing day to day. It is having a negative impact on our youth. Human at possibility of suicide does not want help before trying to attempt and do not feel necessity to any mental health counselling. As a result, suicidal tendency is becoming social challenge. At the same time, social media are becoming popular for communication and exchange emotional expression to the world. Social media like Twitter, Facebook and Instagram play major role for emotion sharing. Therefore, huge number of people publishes their emotion of depression and happiness within notes in these social media like Twitter. Social platform like Twitter has a large number of collection of emotional notes. In this situation, machine learning helps in early prediction of the depression and suicidal tendency. Therefore, in this paper, we develop a soft solution which is able to early detect suicidal tendency among the youth. In this approach, we train a model with genuine suicidal notes and post collected from different sources and generate score for input social media tweet high or low for social tendency prediction. With the help of social media postings like Twitter, we are able to identify risk of suicide.
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References
Marres, N., Gerlitz, C.: Interface methods: renegotiating relations between digital social research. STS and sociology. Sociol. Rev. 64(1), 21–46 (2016). https://doi.org/10.1111/1467-954x.12314
Borra, E., Rieder, B.: Programmed method: developing a toolset for capturing and analyzing tweets. Aslib J. Inf. Manage. 66(3), 262–278 (2014). https://doi.org/10.1108/ajim-09-2013-0094
Marres, N., Moats, D.: Mapping controversies with social media: the case for symmetry. SSRN Electron. J. (2015). https://doi.org/10.2139/ssrn.2567929
Christensen, H., Batterham, P., O’Dea, B.: E-health interventions for suicide prevention. Int. J. Environ. Res. Pub. Health 11(8), 8193–212 (2014). https://doi.org/10.3390/ijerph110808193
Namratha, P., Kishor, M., Sathyanarayana Rao, T.S., Raman, R.: Mysore study: a study of suicide notes. Indian J. Psychiatry 57(4), 379–382 (2015)
Synnott, J., Ioannou, M., Coyne, A., Hemingway, S.: A content analysis of online suicide notes: attempted suicide versus attempt resulting in suicide. Suicide Life Threat. Behav. 48(6), 767–778 (2017). https://doi.org/10.1111/sltb.12398
Prokofyeva, T. (2013). Language use in two types of suicide texts. Published MA thesis. Linkoping University
Robinson, J., Cox, G., Bailey, E., Hetrick, S., Rodrigues, M., Fisher, S., Herrman, H.: Social media and suicide prevention: a systematic review. Early Intervention Psychiatry 10(2), 103–121 (2015). https://doi.org/10.1111/eip.12229
Hamilton, D.R.: Suicide as an escape from pain: an analysis of suicide notes and case files. Browse all Theses and Dissertations. 667.https://corescholar.libraries.wright.edu/etd_all/667 (2012)
Berti, D.: Suicide notes under judicial scrutiny in India. South Asia Multidisc. Acad. J. [Online], 17 — 2018, Online since 19 February 2018, connection on 30 April 2019. http://journals.openedition.org/samaj/4481; https://doi.org/10.4000/samaj.4481
Rani, M., Girdhar, S., Murty, O.: Suicide note: the last words. J. Forensic Med. Toxicol. 32(2), 35–41 (2015)
Pestian, J.P., et al.: A machine learning approach to identifying the thought markers of suicidal subjects: a prospective multicenter trial. Suicide Life Threat. Behav. 47(1), 112–121 (2017)
Gomez, J.M.: Language technologies for suicide prevention in social media. In: Workshop on Natural Language Processing in the5th Information Systems Research Working Days, pp. 21-29, Quito, Ecuador (2014)
Jashinsky, J., Burton, S.H., Hanson, C.L., West, J., Giraud-Carrier, C., Barnes, M.D., et al.: Tracking suicide risk factors through Twitter in the US. Crisis 35(1), 51–9 (2014)
The Suicide Project—Share Your Suicide Stories. Suicideproject.org. Retrieved Oct, 2019 from http://suicideproject.org (2004)
A Collection of Suicide Notes & Letters. Published in a weblog: http://russelljohn.net/journal/2008/03/a-collection-of-suicide-notes
R API for Collecting tweets of individuals. https://github.com/ropensci/rtweet
Briscoe, T., Medlock, B., Andersen, Ø.E.: Automated assessment of ESOL free text examinations. Technical Report UCAM-CL-TR- 790, University of Cambridge, Computer Laboratory (2010)
Shermis, Mark D.: Contrasting state-of-the-art in the machine scoring of short-for constructed responses. Educ. Assess. 20(1), 46–65 (2015)
Crossley, S., Allen, L.K., Snow, E.L., McNamara, D.S.: Pssst... textual fea- tures... there is more to automatic essay scoring than just you! In: Proceedings of the fifth international conference on learning analytics and knowledge, pp. 203–207. ACM (2015)
Zhang, X., Fuehres, H., Gloor, P.A.: Predicting stock market indicators through Twitter “I hope it is not as bad as I fear.” Retrieved from http://dx.doi.org/10.1016/j.sb-spro.2011.10.562
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Sharma, M., Pant, B., Singh, V., Kumar, S. (2021). STP:Suicidal Tendency Prediction Among the Youth Using Social Network Data. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_17
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DOI: https://doi.org/10.1007/978-981-15-4851-2_17
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