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@article{202729,
        author = {Dr. Syed Anis Haider and Dr. Amar Kumar},
        title = {Mental Health Monitoring through Social Media: Using AI to analyze language patterns to detect early signs of depression, anxiety or stress in young adults},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8445-8460},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202729},
        abstract = {In the digital era, mental health issues like depression, anxiety, and chronic stress have become much commonplace among young adults, posing significant psychological and public health concerns on a global scale. At the same time, social media platforms like Instagram, X, Reddit etc. have provided abundant amounts of social media-generated textual data that capture emotional, behavioral and cognitive states. These are online forms that give good information on recognising early signs of mental distress. Artificial Intelligence (AI), including Natural Language Processing (NLP), Machine Learning (ML) and Deep Learning (DL), has been proven to be a useful strategy within computational sciences for interpreting such communication patterns and predicting mental health conditions (Chancellor & De Choudhury, 2020).
The current study investigates the potential of Language Analysis based on AI to identify depression, anxiety and stress in young adults using social media. Research is focused on such linguistic indicators of negative emotional language, hopelessness, social withdrawal, stress related expressions, and suicidal ideation. The performances of the aforementioned advanced AI models like BERT, GPT, RNN and LSTM have been promising in the capacity to identify very fine-grained emotional and psychological cues in online communication (Aldarwish & Ahmad, 2017; Sawhney et al., 2021).
The study adopts a secondary literature-based qualitative and analytical approach to assess the success and limitations of AI-driven systems for mental health surveillance and monitoring systems from 2020 to 2026. Results indicate that AI aids in early detection, ongoing emotional evaluations and in prevention mental health care, more efficiently than numerous conventional approaches (Guntuku et al., 2019). But there are still privacy, consent, algorithmic bias and ethical limits that must be taken into account (Birhane et al., 2022). The study underscores the need for ethical governance, transparency, and human clinical oversight in the responsible use of AI in mental health care.},
        keywords = {Artificial Intelligence, Mental Health Monitoring, Social Media Analytics, Natural Language Processing, Machine Learning, Depression Detection.},
        month = {May},
        }