Impact of Social Media on Mental Health Using AI-Based Sentiment Analysis
Keywords:
Social Media, Mental Health, Sentiment Analysis, Artificial Intelligence, Natural LanguageAbstract
The article examines how social media affects mental health in a multi-faceted manner by integrating the traditional analysis based on a survey method with the latest artificial intelligence (AI) methods. We are not just looking at the frequency and duration of social media usage among people, but also at the type of content that causes distress, the perception discrepancies between age groups, and how AI instruments can be used to diagnose people who might be at risk. The primary data is combined with secondary data to create blending data. Surveys using large-scaled social media samples, and using techniques such as sentiment analysis and natural language processing (NLP), the research study will reveal significant trends between online behavior and psychological performance.The findings point to obvious correlations between the high usage and the negative feelings, as well as reveal the fact that the content type and age are important factors in the formation of experiences. Notably, the study shows that AI-based practices can help in the early identification and intervention. The paper ends by summarizing the practical approaches to more healthy digital interactions and recommending interdisciplinary cooperation to minimize the negative impact of social media on mental health and maintain all its positive aspects.
References
H. Aulia, M. Zulfadhilah, S. E. Prastya, and M. S. Pebriadi, “Analyzing public sentiment towards mental health on social media Twitter using machine learning,” POSITIF: Jurnal Sistem dan Teknologi Informasi, vol. 10, no. 2, 2024.
M. Barai and G. Soni, “The application of sentiment analysis in mental health monitoring and support,” Journal of Android and iOS Applications and Testing, vol. 10, no. 1, pp. 34–55, 2025.
F. Benrouba and R. Boudour, “Emotional sentiment analysis of content on social media in mental health safety contexts, Social Network Analysis and Mining, vol. 13, no. 17, 2023.
L. Braghieri, “Social media and mental health,” American Economic Review, vol. 112, no. 5, pp. 1576–1602, 2022.
R. B. Correia, I. B. Wood, J. Bollen, and L. M. Rocha, “Mining social media data for biomedical signals and health-related behavior,” arXiv preprint arXiv:2001.10285v1, pp. 1–28, 2020.
P. Dewi Pamungkasari, S. Ningsih, A. P. Rifai, A. S. Nandila, H. T. Nguyen, and S. K. Penchala, “Twitter sentiment analysis of mental health issues post COVID-19,” Green Intelligent Systems and Applications, vol. 5, no. 1, pp. 51–60, 2025.
T. Gawas and B. Makwana, “Research on sentimental analysis on mental health using social media,” International Journal
of Scientific Research and Technology, vol. 2, no. 3, pp. 41–47, 2025.
R. Ivic, “Messaging and information in mental health communication: A content analysis,” JMIR Infodemiology, vol. 5, no. 1, e48230, 2025.
P. M. Jadhav, Sonia, and A. N. Kulkarni, “Depression predicting model of emotions of social media user with the help of deep learning methods, Journal of Advanced Zoology, vol. 44, 2024.
V. Khandelwal, M. Gaur, U. Kursuncu, V. Shalin, and A. Sheth, “A domain-agnostic neurosymbolic model of big social data analysis: Mental health sentiment on social media during COVID-19 arxiv preprint arXiv:2411.07163v1, 2024.
J. Z. Liang, “Constructing a mental health analysis system for social media using large language models,” Advances in Engineering Innovation, vol. 16, no. 1, pp. 13–22, 2025.
Y. Liu, Y. Wang, Y. Zhao, and Z. Li, “Transgender community sentiment analysis from social media data: A natural language processing approach,” arXiv preprint arXiv:2010.13062v2, pp. 1–6, 2020.
A. M. Magar, V. Kharosekar, A. Patil, S. Lakade, and R. Sangolgi, “Depression and stress monitoring system via social media data using deep learning framework: A review,” International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 2, no. 5, May 2022.
S. N. Mohd Rum, N. F. A. Saharudin, N. A. Husin, and A. Akbar, “Uncovering depression on social media using BERT model,” Journal of Advanced Research Design, vol. 129, no. 1, pp. 46– 59, 2025.
J. A. Naslund, “Social media and mental health: Benefits, risks, and opportunities for research and practice,” Psychiatric Services, vol. 71, no. 6, pp. 591–598, 2020.
K. D. Odja, Mental illness detection with sentiment analysis in social media, Procedia Computer Science, vol. 187, pp. 133 140, 2024.
R. Plackett, The role of interventions of using social media in mental health: A systematic review, Journal of Medical Internet Research, vol. 25, no. 1, e44922, 2023.
A. Rajput, “Natural language processing, sentiment analysis and clinical analytics,” arXiv preprint arXiv:1902.00679v1, pp. 1–24, 2019.
H. Shao, M. Zhu, and S. Zhai, Mental health diagnosis in the digital age: Harnessing sentiment analysis on social media platforms upon ultra-sparse feature content arXiv preprint arXiv:2311.05075v1, pp. 121, 2023.
I. Villanueva-Miranda, Sentiment analysis in the field of public health: A systematic review, Journal of Medical Internet Research, vol 27, no. 2, e12262, 2025.
H. Wijaya, M. Fawazi Hadi, and N. Sulistianingsih, “Using sentiment analysis with BERT and SVM for detect mental health detection on social media,” Journal of Digital Health Innovation and Medical Technology, vol. 1, no. 2, 2025.
S. Wu, X. Huang, and D. Lu, “Psychological health knowledge-enhanced LLM-based social network crisis intervention text transfer recognition method,” arXiv preprint arXiv:2504.07983v2, pp. 1–16, 2024.
A. Yadav, A. Tiwari, A. Kumar, and A. K. Verma, “Social media mental health detection with NLP and machine learning: The text mining methodology of the IJRASET Journal of Research in Applied Science and Engineering Technology, 2025.
Á. Zsila, “Pros & cons: Impacts of social media on mental health,” BMC Psychology, vol. 11, no. 1, p. 1, 2023.
