Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 12 references
Abstract
Mental health disorders such as suicidal ideation, bipolar disorder, depression, and anxiety pose public health challenges in Turkey. Approximately 20% of the population is estimated to be affected by some form of mental illness. Social media is used for sharing personal experiences and opinions in Turkey. This presents an opportunity to use textual data for detecting mental health disorders. This study aims to examine the predictive performance of various machine learning classifiers for identifying mental health conditions in Turkish language texts from social media. Given the lack of structured and unstructured datasets focused on Turkish mental health, we have mined and processed textual data from the social media platform Reddit. This led to the creation of the Turkish Mental Health Disorder (TMHD) Corpus. The TMHD dataset and the machine learning experiments conducted aim to improve the understanding of predicting mental health issues in Turkey using Artificial Intelligence.
A novel machine learning-driven predictive computational model based on DistilBERT for automated multi-class mental health identification from Reddit posts is proposed in this paper and indicates that transformer-based models are robust and scalable solutions for real-world mental health testing and decision-support sy...
Radhey Shyam, Prashant Yadav, Pawan Thakur· International journal on eme...· 0 citations
This study aims to build a classification model for the early screening of mental health disorders from social media text data using the CRISP-DM framework and implemented into a web-based application as an indicative early detection tool, not a clinical medical diagnosis.
Erna Daniati, Sherly Dian Tiara, Arie Nugroho· The Indonesian Journal of Co...· 0 citations
Mental illness is a major world health issue, with increasing rates of depression, anxiety, and other psychiatric disorders affecting millions of people worldwide. Early diagnosis is crucial in preventing violent presentation and maximizing patient outcomes. However, conventional diagnosis largely relies on self-report...
A. Agyeman· Magna Scientia Advanced Rese...· 0 citations
Mental health problems adversely affect a person's emotional, social, psychological, and well-being. Individuals with mental health issues often do not seek professional help due to social stigma. Social media can serve as a vital tool for assessing a person’s mental health, and several methods have been proposed for c...
Ramesh Singh Saud· Journal of Kapan Multiple Ca...· 0 citations
This work forms social-media-based risk identification as a multi-label text classification problem, enabling the simultaneous prediction of co-existing risks within a single post, and benchmarks multiple transformer-based encoders under a unified multi-label training protocol and reports macro/micro F1 performance, hi...
Z. Özer, Emre Kenger, Görkem Gözükara et al.· El-Cezeri Fen ve Mühendislik...· 0 citations
A comparative analysis of user sentiment on X regarding public opinion on stunting using three widely utilized text classification algorithms: K-Nearest Neighbour, Naïve Bayes, and Decision Tree demonstrates the effectiveness of the Naïve Bayes algorithm for sentiment analysis in public health contexts.
Vivian Collahua Rupaylla, Richard Santiago Quivio Cuno, Sandra Guadalupe et al.· Journal of Intelligent Decis...· 0 citations
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