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Data-Driven Analysis of Mental Health Trends Using Social Media and Public Survey Data
Subject area: Science,Engineering and Technology · Area of research: Computational Mental Health
Abstract
This research paper presents a comprehensive data analysis of mental health trends using social media platforms and publicly available survey datasets. The study leverages sentiment analysis and natural language processing techniques to evaluate public discourse around mental health-related keywords. By integrating insights from social media text and formal survey data, the project identifies common emotional patterns, peak periods of distress, and demographic distributions of mental health concerns. The findings emphasize the importance of early detection, policy intervention, and public awareness.
Keywords
Mental Health, Sentiment Analysis, Social Media, Survey Data, Data Mining, NLP, Public Health Trends.
References
[1] S. Guntuku, M. Yaden, M. Kern, D. Ungar, and L. Eichstaedt, “Detecting depression and mental illness on social media: An integrative review,” Current Opinion in Behavioral Sciences, vol. 18, pp. 43–49, June 2017.
[2] C. Resnik, W. Armstrong, and T. Claudino, “Using topic modeling to improve prediction of neuroticism and depression in college students,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1348–1353, Lisbon, Portugal, Sep. 2015.
[3] M. De Choudhury, S. Counts, and E. Horvitz, “Predicting postpartum changes in emotion and behavior via social media,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267–3276, ACM, Apr. 2013.
[4] M. Saha, D. Weber, “A Social Media-Based Examination of the Effects of Counseling Recommendations After Student Deaths on College Campuses,” Journal of American College Health, vol. 67, no. 3, pp. 247–256, 2019.
[5] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, Jan. 2013.
How to cite this paper
@article{1709632,
author = {Manav Doshi},
title = {Data-Driven Analysis of Mental Health Trends Using Social Media and Public Survey Data},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {1},
pages = {570-573},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709632.pdf},
abstract = {This research paper presents a comprehensive data analysis of mental health trends using social media platforms and publicly available survey datasets. The study leverages sentiment analysis and natural language processing techniques to evaluate public discourse around mental health-related keywords. By integrating insights from social media text and formal survey data, the project identifies common emotional patterns, peak periods of distress, and demographic distributions of mental health concerns. The findings emphasize the importance of early detection, policy intervention, and public awareness.},
keywords = {Mental Health, Sentiment Analysis, Social Media, Survey Data, Data Mining, NLP, Public Health Trends.},
month = {July},
}