A Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community

Authors

DOI:

https://doi.org/10.5753/isys.2022.2285

Keywords:

Depression, Social Networks, Mental Health Informatics

Abstract

Understanding individuals, social dynamics, and data consumption within social media platforms arouse curiosity and attention in the scientific community and society. The scientific community has shown how a user's mental health can be affected by technology and its digital environment. For example, a user exposed to constant explicit hate speech may suffer an impact on its well-being. There are already efforts in this research area that propose automated solutions to identify users who require professional health attention. However, these solutions do not frequently use the experience and background from the health acknowledgment area in their contribution construction. To fill this gap, we propose a qualitative feature validation with two stages to identify which characteristics are relevant to health professionals, aiming at machine learning and deep learning solutions to depression detection. First, we validate this set of features using a semi-structured interview with three psychologists. Afterward, we apply a survey with domain experts to validate the information extracted from the first stage. This feature validation will allow us to have a detailed view of how functional and practical are the features commonly used in machine-learning-based solutions and how they are close to clinical analysis.

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References

Akay, A., Dragomir, A., and Erlandsson, B. E. (2016). Assessing Antidepressants Using Intelligent Data Monitoring and Mining of Online Fora. IEEE Journal of Biomedical and Health Informatics, 20(4):977–986.

Andalibi, N., Ozturk, P., and Forte, A. (2017). Sensitive Self-disclosures, Responses, and Social Support on Instagram: The Case of #Depression. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing -CSCW ’17, pages 1485–1500, Portland, Oregon, USA. ACM Press.

Association, A. P. et al. (2013). Diagnostic and statistical manual of mental disorders(DSM-5®). American Psychiatric Pub.

Bagroy, S., Kumaraguru, P., and De Choudhury, M. (2017). A Social Media Based Index of Mental Well-Being in College Campuses. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, pages 1634–1646, New York,NY, USA. ACM.

Boscarioli, C., Araujo, R. M., and Maciel, R. S. P. (2017).I GranDSI-BR. Number December.

Brody, D. J., Pratt, L. A., and Hughes, J. P. (2018). Prevalence of depression among adults aged 20 and over: United States, 2013-2016. US Department of Health and Human Services, Centers for Disease Control.

Cafezeiro, I., Viterbo, J., Costa, L., Salgado, L., Rocha, M., and Monteiro, R. (2017).Strengthening of the Sociotechnical Approach in Information Systems Research, pages133–147.

Chen, X., Sykora, M. D., Jackson, T. W., and Elayan, S. (2018). What about Mood Swings. In Companion of the The Web Conference 2018 on The Web Conference 2018- WWW ’18, pages 1653–1660, New York, New York, USA. ACM Press.

Chomutare, T., Arsand, E., and Hartvigsen, G. (2015). Mining Symptoms of Severe Mood Disorders in Large Internet Communities. In 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pages 214–219, Sao Carlos, Brazil. IEEE.

Dao, B., Nguyen, T., Venkatesh, S., and Phung, D. (2015). Nonparametric discovery of online mental health-related communities. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pages 1–10, Campus des Cordeliers,Paris, France. IEEE.

Dao, B., Nguyen, T., Venkatesh, S., and Phung, D. (2016a). Discovering latent affective dynamics among individuals in online mental health-related communities. In 2016IEEE International Conference on Multimedia and Expo(ICME), pages 1–6, Seattle,WA, USA. IEEE.

Dao, B., Nguyen, T., Venkatesh, S., and Phung, D. (2016b). Effect of social capital on emotion, language style and latent topics in online depression community. In 2016 IEEE RIVF Int. Conf. on Computing & Communication Technologies, Research, Inno-vation, and Vision for the Future (RIVF), pages 61–66, Hanoi, Vietnam. IEEE.

De Choudhury, M., Counts, S., and Horvitz, E. (2013a). Major life changes and behavioral markers in social media: case of childbirth. InProc. of the 2013 conference on Computer supported cooperative work - CSCW ’13, page 1431, San Antonio, Texas,USA. ACM Press.

De Choudhury, M., Counts, S., and Horvitz, E. (2013b). Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’13, CHI ’13, page 3267, New York,New York, USA. ACM Press.

De Choudhury, M., Counts, S., and Horvitz, E. (2013c). Social Media As a Measurement Tool of Depression in Populations. In Proceedings of the 5th Annual ACM Web Science Conference, WebSci ’13, pages 47–56, New York, NY, USA. ACM.

De Choudhury, M., Sharma, S. S., Logar, T., Eekhout, W., and Nielsen, R. C. (2017).Gender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW ’17, pages 353–369, New York, NY, USA. ACM.

Elkin, N. (2008). How america searches: Health and wellness. Opinion Research Corporation: iCrossing, pages 1–17.

Fang, Y.-E., Tai, C.-H., Chang, Y.-S., and Fan, C.-T. (2014). A mental disorder early warning approach by observing depression symptom in social diary. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2060–2065,San Diego, CA, USA. IEEE.

Homan, C. M., Lu, N., Tu, X., Lytle, M. C., and Silenzio, V. M. B. (2014). SocialStructure and Depression in TrevorSpace. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW ’14, pages 615–625, New York, NY, USA. ACM.

Horvitz, E. and Mulligan, D. (2015). Data, privacy, and the greater good. Science, 349(6245):253–255.

James, S. L., Abate, D., Abate, K. H., Abay, S. M., Abbafati, C., Abbasi, N., Abbastabar,H., Abd-Allah, F., Abdela, J., Abdelalim, A., Abdollahpour, I., Abdulkader, R. S.,Abebe, Z., Abera, S. F., Abil, O. Z., Abraha, H. N., Abu-Raddad, L. J., Abu-Rmeileh,N. M. E., Accrombessi, M. M. K., and Acharya, D. (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. The Lancet, 392(10159):1789–1858.

Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., and Kaewpitakkun, Y. (2018). Facebook Social Media for Depression Detection in the Thai Community. In 2018 15th International Joint Conference on Computer Science and Software Engineering(JCSSE), pages 1–6, Nakhonpathom, Thailand. IEEE.

Kavuluru, R., Ramos-Morales, M., Holaday, T., Williams, A. G., Haye, L., and Cerel, J.(2016). Classification of Helpful Comments on Online Suicide Watch Forums. In Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB ’16, pages 32–40, New York, NY, USA. ACM.

Larsen, M. E., Boonstra, T. W., Batterham, P. J., O’Dea, B., Paris, C., and Christensen,H. (2015). We Feel: Mapping Emotion on Twitter. IEEE Journal of Biomedical and Health Informatics, 19(4):1246–1252.

Lech, M., Low, L.-S., and Ooi, K. E. (2014). Detection and prediction of clinical depression. In Mental Health Informatics, pages 185–199. Springer.

Leitão, C. F. and Prates, R. O. (2017).A aplicação de métodos qualitativos em computação. Jornadas de Atualização em Informática, 2017:43–90

Li, G., Zhou, X., Lu, T., Yang, J., and Gu, N. (2016). SunForum: Understanding Depression in a Chinese Online Community. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing - CSCW 16, pages 514–525, New York, New York, USA. ACM Press.

Nakagawa, E. Y., Scannavino, K. R. F., Fabbri, S. C. P. F., and Ferrari, F. C. (2017).Revisão sistemática da literatura em engenharia de software: teoria e prática. Elsevier Brasil.

Nambisan, P., Luo, Z., Kapoor, A., Patrick, T. B., and Cisler, R. A. (2015). Social Media, Big Data, and Public Health Informatics: Ruminating Behavior of Depression Revealed through Twitter. In 2015 48th Hawaii International Conference on System Sciences, pages 2906–2913, HI, USA. IEEE.

Nguyen, T., Phung, D., Dao, B., Venkatesh, S., and Berk, M. (2014). Affective and Content Analysis of Online Depression Communities. IEEE Transactions on Affective Computing, 5(3):217–226.

Nobles, A. L., Glenn, J. J., Kowsari, K., Teachman, B. A., and Barnes, L. E. (2018).Identification of Imminent Suicide Risk Among Young Adults Using Text Messages. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems,CHI ’18, pages 413:1—-413:11, New York, NY, USA. ACM.

Oyong, I., Utami, E., and Luthfi, E. T. (2018). Natural Language Processing and Lexical Approach for Depression Symptoms Screening of Indonesian Twitter User. In201810th International Conference on Information Technology and Electrical Engineering(ICITEE), pages 359–364, Kuta. IEEE.

Park, S., Kim, I., Lee, S. W., Yoo, J., Jeong, B., and Cha, M. (2015). Manifestation of Depression and Loneliness on Social Networks: A Case Study of Young Adults on Facebook. InProc. of the 18th ACM Conf. on Computer Supported Cooperative Work & Social Computing, CSCW ’15, pages 557–570, New York, NY, USA. ACM.

Sadeque, F., Xu, D., and Bethard, S. (2018). Measuring the Latency of Depression Detection in Social Media. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM ’18.

Saha, B., Nguyen, T., Phung, D., and Venkatesh, S. (2016). A framework for classifying online mental health-related communities with an interest in depression. IEEE Journalof Biomedical and Health Informatics, 20(4):1008–1015.

Silveira Fraga, B., da Silva, A. P., and Murai, F. (2018). Online Social Networks in HealthCare: A Study of Mental Disorders on Reddit. In2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pages 568–573, Santiago. IEEE.

Simms, T., Ramstedt, C., Rich, M., Richards, M., Martinez, T., and Giraud-Carrier, C.(2017). Detecting Cognitive Distortions Through Machine Learning Text Analytics.In 2017 IEEE International Conference on Healthcare Informatics (ICHI), pages 508–512, Park City, UT, USA. IEEE.

Trotzek, M., Koitka, S., and Friedrich, C. M. (2018). Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences. IEEE Transactions on Knowledge and Data Engineering, page 1.

Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., and Ohsaki, H. (2015). Recognizing Depression from Twitter Activity. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15, pages 3187–3196.

Vedula, N. and Parthasarathy, S. (2017). Emotional and Linguistic Cues of Depression from Social Media. Proceedings of the 2017 International Conference on Digital Health - DH ’17, pages 127–136.

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., and Ho, R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (covid-19) epidemic among the general population in china. International journal of environmental research and public health, 17(5):1729.

Weiss, R. S. (1995). Learning from Strangers: The Art and Method of Qualitative Interview Studies.

Wilson, M. L., Ali, S., and Valstar, M. F. (2014). Finding Information About Mental Health in Microblogging Platforms: A Case Study of Depression. In Proceedings of the 5th Information Interaction in Context Symposium, IIiX ’14, pages 8–17, New York, NY, USA. ACM.

Wohlin, C., Runeson, P., H ̈ost, M., Ohlsson, M. C., Regnell, B., and Wessl ́en, A. (2012).Experimentation in software engineering, volume 9783642290.

Wongkoblap, A., Vadillo, M. A., and Curcin, V. (2018). A Multilevel Predictive Model for Detecting Social Network Users with Depression. In2018 IEEE International Conference on Healthcare Informatics (ICHI), pages 130–135, New York, NY. IEEE.

Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan,K., Pathak, J., and Sheth, A. (2017). Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM ’17, pages 1191–1198, New York, NY, USA. ACM.

Zhao, X., Lin, S., and Huang, Z. (2018). Text Classification of Micro-blog’s ”Tree Hole”Based on Convolutional Neural Network. In Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2018, pages61:1—-61:5, New York, NY. ACM.

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Published

2022-10-18

How to Cite

Pereira Lima Filho, S., Ferreira da Silva, M., Oliveira Sampaio, J., & Couto Ruback Rodrigues, L. (2022). A Study about Gathering Features in Depression Detection’ Problem with Health Professionals Community. ISys - Brazilian Journal of Information Systems, 15(1), 10:1–10:25. https://doi.org/10.5753/isys.2022.2285

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