Abstract
Mental health disorders, also known as mental illnesses, are the conditions that most affect and paralyze millions of people around the world. One of the symptoms of people with mental health disorders has a habit of wanting to be alone. This causes mental patients to seek online media channels such as Facebook and Twitter to share their thoughts. The experiences gained from social media become a source of information that can be accessed openly and contains reliable information and behavioral patterns that affect the personality and psychology of users. Early detection and intervention is the best solution in treatment and care to deal with this health disorder. However, accurate and non-invasive early detection of mental health disorders is proving difficult. The purpose of this article is to conduct a literature study of classification algorithms to predict, prevent, and detect mental health disorders. Some of the algorithms studied include Random Forest, SVM, and Deep Learning.
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References
Chancellor, S., Birnbaum, M.L., Caine, E.D., Silenzio, V.M., De Choudhury, M.: A taxonomy of ethical tensions in inferring mental health states from social media. In: Proceedings of the conference on fairness, accountability, and transparency, pp. 79–88 (2019)
Chancellor, S., Birnbaum, M.L., Caine, E.D., Silenzio, V.M., De Choudhury, M.: A taxonomy of ethical tensions in inferring mental health states from social media. In: Proceedings of the conference on fairness, accountability, and transparency, pp. 79–88 (2019)
Conway, M., O’Connor, D.: Social media, big data, and mental health: current advances and ethical implications. Curr. Opin. Psychol. 9, 77–82 (2016)
Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. Int. J. Multimedia Ubiquitous Eng. 10(4), 215–230 (2015)
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)
Hassan, A.U., Hussain, J., Hussain, M., Sadiq, M., Lee, S.: Sentiment analysis of social networking sites (SNS) data using machine learning approach for the measurement of depression. In: 2017 International Conference on Information and Communication Technology Convergence (ICTC), pp. 138–140. IEEE (2017)
Islam, M.R., Kabir, M.A., Ahmed, A., Kamal, A.R.M., Wang, H., Ulhaq, A.: Depression detection from social network data using machine learning techniques. Health Inf. Sci. Syst. 6(1), 1–12 (2018). https://doi.org/10.1007/s13755-018-0046-0
Joshi, D.J., Makhija, M., Nabar, Y., Nehete, N., Patwardhan, M.S.: Mental health analysis using deep learning for feature extraction. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 356–359 (2018)
Katchapakirin, K., Wongpatikaseree, K., Yomaboot, P., Kaewpitakkun, Y.: Facebook social media for depression detection in the that community. In: 2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1–6. IEEE (2018)
Khan, S.I., Islam, A., Hossen, A., Zahangir, T.I., Hoque, A.S.M.L.: Supporting the treatment of mental diseases using data mining. In: 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 339–344. IEEE (2018)
Li, A., Jiao, D., Zhu, T.: Detecting depression stigma on social media: a linguistic analysis. J. Affect. Disord. 232, 358–362 (2018)
Moers, T., Krebs, F., Spanakis, G.: Semtec: social emotion mining techniques for analysis and prediction of Facebook post reactions. In: International Conference on Agents and Artificial Intelligence, pp. 361–382. Springer (2018)
Nalinde, P.B., Shinde, A.: Machine learning framework for detection of psychological disorders at osn. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(11) (2019)
Shen, G., Jia, J., Nie, L., Feng, F., Zhang, C., Hu, T., Chua, T.S., Zhu, W.: Depression detection via harvesting social media: a multimodal dictionary learning solution. In: IJCAI, pp. 3838–3844 (2017)
Shuai, H.H., Shen, C.Y., Yang, D.N., Lan, Y.F.C., Lee, W.C., Philip, S.Y., Chen, M.S.: A comprehensive study on social network mental disorders detection via online social media mining. IEEE Trans. Knowl. Data Eng. 30(7), 1212–1225 (2017)
Shyamasundar, L., Rani, P.J.: Twitter sentiment analysis with different feature extractors and dimensionality reduction using supervised learning algorithms. In: 2016 IEEE Annual India Conference (INDICON), pp. 1–6. IEEE (2016)
Syarif, I., Ningtias, N., Badriyah, T.: Study on mental disorder detection via social media mining. In: 2019 4th International Conference on Computing, Communications and Security (ICCCS), pp. 1–6. IEEE (2019)
Thorstad, R., Wolff, P.: Predicting future mental illness from social media: A big-data approach. Behav. Res. Methods 51(4), 1586–1600 (2019). https://doi.org/10.3758/s13428-019-01235-z
Yoo, S., Song, J., Jeong, O.: Social media contents based sentiment analysis and prediction system. Expert Syst. Appl. 105, 102–111 (2018)
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Maulana, A.T. et al. (2023). Analyze Mental Health Disorders from Social Media: A Review. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_5
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