Characterization of Anxiety, Depression, and their Comorbidity from Texts of Social Networks


Depression has become a public health issue, and the high comorbidity rate with anxiety worsens the clinical picture. Early identification is crucial for decisions on the proper line of treatment. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. This paper explores deep learning techniques to develop an ensemble stacking classifier for the automatic identification of depression, anxiety, and their comorbidity, using a self-diagnosed dataset extracted from Reddit. At the lowest level, binary classifiers make predictions about specific disorders, outperforming all baseline models. A meta-learner explores these weak classifiers as a context for reaching a multi-label decision, achieving a Hamming Loss of 0.29 and Exact Match Ratio of 0.47. We performed a qualitative analysis using SHAP, which confirmed the relationship between the influential features and symptoms of these disorders.

Palavras-chave: Deep Learning, Ensemble, Social Networks, Mental Health


American Psychiatric Association (2013).Diagnostic and statistical manual of mentaldisorders: DSM-5. Autor, Washington, DC, 5th ed. edition.

Cohan, A., Desmet, B., Yates, A., Soldaini, L., MacAvaney, S., and Goharian, N. (2018).SMHD: a large-scale resource for exploring online language usage for multiple men-tal health conditions. In Bender, E. M., Derczynski, L., and Isabelle, P., editors,Pro-ceedings of the 27th International Conference on Computational Linguistics, COLING2018, Santa Fe, New Mexico, USA, August 20-26, 2018, pages 1485–1497. Associationfor Computational Linguistics.

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 Workand Social Computing, CSCW ’17, page 353–369, New York, NY, USA. Associationfor Computing Machinery.

Dutta, S.; Ma, J.; De Choudhury, M.. Measuring the Impact of Anxiety on Online Social Interactions. International AAAI Conference on Web and Social Media, North America, jun. 2018.

Hirschfeld, R. (2001). The comorbidity of major depression and anxiety disorders: Recognition and management in primary care. Prim Care Companion J Clin Psychiatry, 3(244-254).

Lundberg, S. M. and Lee, S. (2017). A unified approach to interpreting model predictions. In Guyon, I., von Luxburg, U., and et alli, editors, Advances in Neural Information Processing Systems: Proc. of the 30th Annual Conf. on Neural Information Processing Systems (NIPS), pages 4765–4774.

Mann, P., Paes, A., and Matsushima, E. H. (2020). See and read: Detecting depression symptoms in higher education students using multimodal social media data. Proceed-ings of the International AAAI Conference on Web and Social Media, 14(1):440–451.

Murphy, K. P. (2012).Machine Learning: A Probabilistic Perspective. The MIT Press.

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. CSCW’15, pages 557–570.

Radloff, L. S. (1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3):385–401.

Sharma, E. and De Choudhury, M. (2018). Mental health support and its relationship to linguistic accommodation in online communities. CHI’18, pages 1–13.

Tiller, J. (2013). Depression and anxiety. The Medical Journal of Australia, 199 (6): S28-S31.

Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., and Ohsaki, H. (2015). Recognizing depression from Twitter activity. CHI’15, pages 3187–3196.

Wongkoblap, A., Vadillo, M., and Curcin, V. (2017). Researching mental health disorders in the era of social media: Systematic review. Journal of Medical Internet Research, 19(6).

Yates, A., Cohan, A., and Goharian, N. (2017). Depression and self-harm risk assessment in online forums. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2968–2978, Copenhagen, Denmark. Association for Computational Linguistics.
SOUZA, Vanessa Borba de; NOBRE, Jéferson Campos; BECKER, Karin. Characterization of Anxiety, Depression, and their Comorbidity from Texts of Social Networks. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 35. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 121-132. ISSN 2763-8979. DOI: