Study on the Correlations Between Social Media Use and Mental Health
DOI:
https://doi.org/10.61173/9f0ch671Keywords:
Social Media, Mental Health, self-assess-ment, medicalAbstract
The extensive influence of social media has transformed the way people interact and communicate in the past two decades. However, as concerns regarding mental health increase alongside the rise of social media, there is growing speculation that correlation exists between the two factors. In the light of this issue, researchers have leveraged machine learning algorithms to examine the potential correlations and understand the health implications. In this paper, we employ three ML models and explore a specific aspect of the issue: the relationship between social media use and sleep, a key indicator of mental health conditions. This investigation can be utilized in medical settings as a preliminary self-assessment, providing access to mental health diagnosis for a wider population.
References
[1] Sinnott, R. O., Duan, H., & Sun, Y. (2016). A Case Study in Big Data Analytics. Elsevier EBooks, 357–388.
[2] Hastie, T., Tibshirani, R., & Friedman, J. (2008). Springer Series in Statistics The Elements of Statistical Learning Data Mining, Inference, and Prediction Second Edition.
[3] Chancellor, S., & Munmun De Choudhury. (2020). Methods in predictive techniques for mental health status on social media: a critical review. Npj Digital Medicine, 3(1).
[4] Safa, R., & Sorourkhah, A. (n.d.). Predicting mental health using social media: A roadmap for future development.
[5] Chung, J., & Teo, J. (2022). Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges. Applied Computational Intelligence and Soft Computing, 2022, 1–19.
[6] Breiman, L. (2001). Machine Learning, 45(1), 5–32.
[7] Lior Rokach, & Maimon, O. (2006). Decision Trees. Springer EBooks, 165–192.
[8] Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022). Activation functions in deep learning: A comprehensive survey and benchmark. Neurocomputing, 503, 92–108.
[9] Machine Learning Guide for Oil and Gas Using Python. (2021).
[10] Hercules Dalianis. (2018, May 15). Evaluation Metrics and Evaluation.
[11] Ortiz-Ospina, E., & Roser, M. (2023, December 28). The Dean&Francis ISSN 2959-6149 rise of social media. Our World in Data.
[12] Lin, H., Jia, J., Nie, L., Shen, G., & Chua, T.-S. (2016). What Does Social Media Say about Your Stress?
[13] Digital 2023: Global Overview Report — DataReportal – Global Digital Insights. (2023, January 26). DataReportal – Global Digital Insights. DataReportal – Global Digital Insights.
[14] Chen, S., Li, H., Pang, L., & Wen, D. (2023). The Relationship Between Social Media Use and Negative Emotions Among Chinese Medical College Students: The Mediating Role of Fear of Missing Out and the Moderating Role of Resilience. Psychology Research and Behavior Management, Volume 16, 2755–2766.
[15] Ngumimi Karen Iyortsuun, Kim, S.-H., Jhon, M., Yang, H.- J., & Pant, S. (2023). A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare, 11(3), 285–285.
[16] World Health Organization. (2022, June 8). Mental disorders. Who.int; World Health Organization: WHO.
[17] Social Media and Mental Health. Kaggle.com. https://www. kaggle.com/datasets/souvikahmed071/social-media-and-mentalhealth
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
