Abstract
Mental health illness has become a serious public problem. Finding changes in everyday behavior is a demand. This paper tries to detect persons who have mental health illness using their short comments posted to social network systems. The novelty of this study is using comments in a system for communication between users with mental health illness, in order to prepare a sufficient amount of supervised data for machine learning. The authors used approximately 120,000 comments in the system as positive samples and 120,000 comments in Twitter as negative samples for detecting mental health illness. Both data are posted short comments on a daily basis. The authors conducted a straightforward classification of the comments using a support vector machine and surface-level features of the comments. The accuracy of the classification is 0.92 and the characteristic phrases used for the classification are related to troubles in mental health. The ability to classify everyday statements can be expected to lead to the early detection of mental disorders.
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References
Cocooru. https://cocooru.com. Accessed 13 Sept 2018
Depression, World Health Organization. http://www.who.int/en/news-room/fact-sheets/detail/depression. Accessed Nov 2018
Facebook. https://www.facebook.com. Accessed 6 Dec 2018
Twitter. https://twitter.com. Accessed 13 Sept 2018
Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci. 18, 43–49 (2017). Big data in the behavioural sciences
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, NIPS 2013, vol. 2, pp. 3111–3119. Curran Associates Inc., USA (2013)
Reece, A.G., Danforth, C.M.: Instagram photos reveal predictive markers of depression. EPJ Data Sci. 6(1), 15 (2017)
Reece, A.G., Reagan, A.J., Lix, K.L.M., Dodds, P.S., Danforth, C.M., Langer, E.J.: Forecasting the onset and course of mental illness with twitter data. Sci. Rep. 7(1) (2017)
Wongkoblap, A., Vadillo, M.A., Curcin, V.: Researching mental health disorders in the era of social media: systematic review. J. Med. Internet Res. 19(6), e228 (2017)
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 15H02787.
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Baba, T., Baba, K., Ikeda, D. (2020). Detecting Mental Health Illness Using Short Comments. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_23
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DOI: https://doi.org/10.1007/978-3-030-15032-7_23
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