شناسایی اختلال‌های روان‌شناختی بر اساس داده‌های موجود در محیط‌های مجازی با استفاده از یادگیری ماشین

نویسندگان
مریم ابوریحانی محمدی
مهشاد فدایی مقدم حیدر ابادی
سولماز زرداری
سمیه حیثیت طلب
دانشگاه تبریز
چکیده
اخیراً پژوهش‌هایی در راستای به‌کارگیری شبکه‌های اجتماعی به‌عنوان بستری جدید برای شناسایی افراد با اختلال‌های روانی صورت گرفته است. علاوه بر این به دلیل پیچیدگی تشخیص بیماری‌های روان‌شناختی با استفاده از روش‌های معمول، استفاده از یادگیری ماشین برای شناسایی این افراد رو به افزایش است. هدف این مقاله مرور پژوهش­های انجام شده با استفاده از داده‌های شبکه‌های اجتماعی در پیش‌بینی و تشخیص اختلال‌های روان‌شناختی به کمک یادگیری ماشین است. در این پژوهش به روش مرور نظام‌مند مبتنی بر پریسما، از طریق جستجوی کلید واژه‌های اصلی تشخیص و پیش‌بینی اختلال‌های روان‌شناختی در ترکیب با واژه‌های یادگیری ماشین و فضای مجازی در پایگاه‌های اطلاعاتی تخصصی بدون در نظر گرفتن سال انتشار آن‌ها، یافته‌ها و اطلاعات مورد نظر جهت دستیابی به هدف پژوهش مورد واکاوی قرار گرفت. اختلال روان‌شناختی افسردگی در بین 20 مقاله نهایی انتخاب شده بیشترین فراوانی با قدرت پیش‌بینی 42 و 87 درصد به ترتیب کمترین و بیشترین توان پیش‌بینی افسردگی را داشته است. از طرفی برای جمع‌آوری داده تنها 30 درصد مطالعات از پرسشنامه فضای مجازی و بیشترین رویکرد، از پست‌های عمومی در شبکه‌های اجتماعی با عبارات منظم و نیز توییتر به‌عنوان بیشترین منبع استفاده‌ شده است. به نظر می‌رسد روانشناسی محاسباتی مبتنی بر روش‌های یادگیری ماشین بتواند به شناسایی و پیش‌بینی دقیق‌تر اختلال‌های روان‌شناختی کاربران فضاهای مجازی کمک نماید.


کلیدواژه‌ها
روانشناسی محاسباتی
یادگیری ماشین
شبکه‌های اجتماعی
اختلال‌های روان‌شناختی

عنوان مقاله English

Identifying Psychological Disorders Based on Data in Virtual Environments Using Machine Learning

نویسندگان English

Maryam Aboureihani Mohammadi
Mahshad Fadaei Moghadam Heydarabadi
Solmaz Zardary
Soomaayeh Heysieattalab
چکیده English

Recently, research has been conducted on the use of social networks as a new platform for identifying people with mental disorders. In addition, because of the complexity of diagnosing psychological diseases using conventional methods, the use of machine learning for identifying theses psychological diseases is increasing. The goal of this article was to systematically review the research conducted using social media data for predicting and diagnosing psychological disorders with the help of machine learning. Based on systematic review on the Prisma method, the aim of this article was achieved through searching the main keywords of diagnosis and the prediction of mental disorders combined with machine learning and social media data without considering the dates of their publications. Depression had the highest frequency among the final 20 selected articles with a predictive power of 42% and 87%, the lowest and the highest respectively. On the other hand, only 30% of studies used questionnaires for gathering data on social media and the most common approach for data collection was public posts on social media by the use of regular expressions. Twitter has also been used as the largest source of data collection in these sorts of studies. It seems that computational psychology based on machine learning methods could help to identify disorders at an appropriate time and select more effective treatments for mental disorders among the users of social media.




کلیدواژه‌ها English

Computational Psychology
Machine Learning
Social Media
Psychological Disorders
Birjali, M., Beni-Hssane, A., Erritali, M. (2017). Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks. Procedia Computer Science. 113: 65-72.
Bloom, D.E., Cafiero, E.T., Jané-Llopis, E., Abrahams-Gessel, S., Bloom, L.R., Fathima, S., Feigl, A.B., Gaziano, T., Mowafi, M., Pandya, A., Prettner, K., Rosenberg, L., Seligman, B., Stein, A.Z., & Weinstein, C. (2011). The Global Economic Burden of Noncommunicable Diseases. Geneva: World Economic Forum.
Burnap, P., Colombo, G., Amery, R., Hodorog, A., Scourfield, J. (2017). Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media. 2: 32-44.
Calvo, R.A., Milne, D.N., Hussain, M.S. (2017). Christensen H. Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering. 23(5): 649-85.
Conway, M. (2014). Ethical issues in using Twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature. Journal of medical Internet research. 16(12): e290.
Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K. (2015). From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In Proceedings of the 2nd Workshop on Computational Linguistics


and Clinical Psychology: From Linguistic Signal to Clinical Reality. 1-10.
Coppersmith, G., Harman, C., Dredze, M. (2014). Measuring post-traumatic stress disorder in Twitter. In Eighth international AAAI conference on weblogs and social media
Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning, 20 (3):273-97.
Cumbie, S.A., Conley, V.M., Burman, M.E. (2004). Advanced practice nursing model for comprehensive care with chronic illness: model for promoting process engagement. Advances in Nursing Science. 27(1):70-80.
De Choudhury, M., Counts, S., Horvitz, E.J., Hoff, A. (2014). Characterizing and predicting postpartum depression from shared Facebook data. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. 626-638.
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E. (2013). Predicting depression via social media. In Seventh international AAAI conference on weblogs and social media.
Freyne, J., Coyle, L., Smyth, B., Cunningham, P. (2010). Relative status of journal and conference publications in computer science. Communications of the ACM. 53(11): 124-32.
Goldberg, D.P. The Detection of Psychiatric Illness by Questionnaire. Maudsley Monograph 21 ed.
Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences. 18:43-9.
Islam, M.R., Kabir, M.A., Ahmed, A., Kamal, A.R., Wang, H., Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health information science and systems. 6(1): 8.
Kaplan, H., Sadouk, B. (1996). Abstract of clinical psychiatry. First edition, publication of Hayyan. 153-158.
Kittur, A., Chi, E.H., Suh, B. (2008). Crowdsourcing user studies with Mechanical Turk. InProceedings of the SIGCHI conference on human factors in computing systems. 453-456Kumar, A., Sharma, A., Arora, A. Anxious Depression Prediction in Real-time Social Data. arXiv:1903.10222
Kumar, M., Dredze, M., Coppersmith, G., De Choudhury, M. (2015). Detecting changes in suicide content manifested in social media following celebrity suicides. InProceedings of the 26th ACM conference on Hypertext & Social Media, 85-94.Librenza-Garcia, D., Kotzian, B.J., Yang, J., Mwangi, B, Cao, B., Lima, L.N., Bermudez, M.B., Boeira, M.V., Kapczinski, F., Passos, I.C. (2017). The impact of machine learning techniques in the study of bipolar disorder: a systematic review. Neuroscience & Biobehavioral Reviews. 80: 538-54.
Lin H, Jia J, Guo Q, Xue Y, Li Q, Huang J, Cai L, Feng L. (2014). User-level psychological stress detection from social media using deep neural network. In Proceedings of the 22nd ACM international conference on Multimedia 507-516.
Mesagno C, Mullane-Grant T. (2010). A comparison of different per-performanceRoutines as possible choking interventions. Journal of Applied Sport Psychology. 22(3):343-60.
McCallum, A.k. (2002). Mallet: A machin learning for language toolkit.
http://mallet. cs. Umass.edu.
McKee, R. (2013). Ethical issues in using social media for health and health care research. Health Policy. 110(2-3): 298-301.
Miller, G.A. (1995). WordNet: a lexical database for English. Communications of the ACM. 38(11): 39-41.
Nadeem, M. (2016). Identifying depression on Twitter. ArXiv: preprint, 1607.073.
Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W. (1996). Applied linear statistical models. Models. Chicago: Irwin;
O'Dea, B., Wan, S., Batterham, P.J., Calear, A.L., Paris, C. Christensen, H. )2015). Detecting suicidality on Twitter. Internet Interventions. 2(2), 183-8Orabi, A.H., Buddhitha, P., Orabi, M.H., Inkpen, D. (2018). Deep learning for depression detection of twitter users. InProceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 88-97.
Passos, I.C., Mwangi, B., Vieta, E., Berk, M., Kapczinski, F. (2016). Areas of controversy in neuroprogression in bipolar disorder. Acta Psychiatric a Scandinavia.; 134(2):91-103.
Paul, S., Jandhyala, S.K., Basu, T. (2018). Early Detection of Signs of Anorexia and Depression over Social Media using Effective Machine Learning Frameworks. InCLEF (Working Notes).
Pennebaker, J.W., Francis, M.E., Booth, R.J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates: .71: 2001.
Reece, A.G., Danforth, C.M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science.6(1): 15.
Reece, A.G., Reagan, A.J., Lix, K.L., Dodds, P.S., Danforth, C.M., Langer, E.J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific reports. 7(1), 13006.
Sadock, B. J., Sadock, V. A., Ruiz, P., & Kaplan, H. I. (2015). Kaplan & Sadock's synopsis of psychiatry: Behavioral sciences, clinical psychiatry. Philadelphia: Wolters Kluwer.
Schwartz, H.A., Eichstaedt, J., Kern, M.L., Park, G., Sap, M., Stillwell, D., Kosinski, M., Ungar, L. (2014). Towards assessing changes in degree of depression through Facebook. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 118-125.
Shamir, L. (2010). The effect of conference proceedings on the scholarly communication in Computer Science and Engineering. Scholarly and Research Communication, 1(2).
Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., Chua, T.S., Zhu, W. (2017). Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution. In IJCAI, 3838-3844.
Shuai, H.H., Shen, C.Y., Yang, D.N., Lan, Y.F., Lee, W.C., Philip, S.Y., Chen, M.S. (2017). A comprehensive study on social network mental disorders detection via online social media mining. IEEE Transactions on Knowledge and Data Engineering. 30 (7):1212-25.
Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems, 3187-3196.Veronese, E., Castellani, U., Peruzzo, D., Bellani, M., Brambilla, P. (2013). Machine learning approaches: from theory to application in schizophrenia. Computational and mathematical methods in medicine, 2013.
World Health Organization. Mental health action plan 2013-2020.-
Wang, P.S., Lane, M., Olfson, M., Pincus, H.A., Wells, K.B., Kessler, R.C. (2005). Twelve-month use of mental health services in the United States: results from
the National Comorbidity Survey Replication. Archives of general psychiatry. 62 (6): 629-40.
Whiteford, H.A., Degenhardt, L., Rehm, J., Baxter, A.J., Ferrari, A.J., Erskine, H.E., Charlson, F.J., Norman, R.E., Flaxman, A.D., Johns, N., Burstein, R. (2013). Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study. The Lancet. 382 (9904): 1575-86.
Wongkoblap, A., Vadillo, M.A., Curcin, V. (2017). Researching mental health disorders in the era of social media: systematic review. Journal of medical Internet research. 19(6): e228.
Yasamy, M.T., Sardarpour Goudarzi, S.H., Amin Esmaeeli, M., Mahdavi, N., Ebrahimpour, A. Bagheri yazdi, S.A. (2005). Practical Mental health for general and family particitioner. Tehran: Aramesh.
Zhuang, Z., Elmacioglu, E., Lee, D., Giles, C.L. (2007). Measuring conference quality by mining program committee characteristics. InProceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries. 225-234. ACM.