From Conversations to Insights: Analysing Social Networks for Early Mental Health Detection - A Systematic Review of Causal Inference and Deep Learning

JOURNAL OF COMMUNICATION, LANGUAGE AND CULTURE

Ontology-Driven Depression Detection on Social Media via Large Language Model Reasoning

Early detection of depression is a critical challenge for global mental health, as traditional clinical assessments are often delayed by social stigma and limited medical resources. While social media offers a unique window into real-world behavior, existing automated methods often lack clinical interpretability and symbolic reasoning. This paper proposes a novel framework that integrates a formal depression ontology with Large Language Models (LLMs) to enhance screening accuracy and transparency. By transforming ontological hierarchies and deterministic inference rules into a natural language style representation, we enable LLMs to perform step-by-step symbolic reasoning beyond simple sentiment analysis. Experimental results on a dataset of 200 Twitter users demonstrate that our ontology-driven framework significantly outperforms the baseline, achieving an accuracy of 88.0% and a precision of 91.5%. The model successfully reduces false positives by verifying temporal persistence and providing a traceable reasoning chain from digital footprints to clinical symptoms. This approach offers a promising path toward explainable AI-assisted mental health monitoring.

Bias-Aware Systematic Review of AI-Based Mental Health Detection Using Social Media: A PRISMA and PROBAST(+AI) Analysis

This study presents a bias-aware systematic review of artificial intelligence (AI)-based mental health detection using social media data from 2015 to 2025. Guided by PRISMA 2020 and PROBAST(+AI), records from IEEE Xplore, Scopus, and Web of Science were screened from 3,861 initial records to 308 included studies. Results show a strong shift toward transformer and hybrid models, depression-focused tasks, and Twitter/X and Reddit datasets. However, the corpus also shows major methodological gaps, including absent external validation, limited reporting of class imbalance handling, low explainable AI adoption, and underreported platform sources. The review argues that future systems require transparent data provenance, bias-aware validation, explainable decision support, and privacy-preserving crossplatform evaluation before clinical or public-health deployment.

Artificial Intelligence in Mental Health Care: A Systematic Review of Benefits and Risks for Clinical and Institutional Decision-Making

Stunting is a critical public health issue, particularly in developing countries, with long-term impacts on physical and cognitive development due to chronic malnutrition. In the digital era, public sentiment, and opinion on stunting, as reflected on social media platforms, can provide valuable insights for policymakers and healthcare providers. However, there has been limited research combining multiple machine-learning algorithms for sentiment analysis on this topic. This study aims to conduct a comparative analysis of user sentiment on X regarding public opinion on stunting using three widely utilized text classification algorithms: K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree. The research involves collecting and annotating sentiment data (positive, negative, neutral) from social media platforms like Twitter and X, followed by model training and evaluation using metrics such as accuracy, precision, recall, and F1-score. Results from the analysis of 1227 data points (2023-2024) with an 80:20 train-test split revealed that Naïve Bayes achieved the highest accuracy at 87.18%, followed by Decision Tree at 79.83%, and KNN at 57.56%. Meanwhile, a comparison using a 70:30 train-test split yielded similar results, with Naïve Bayes achieving 86.41%, Decision Tree 78.41%, and KNN 54.48%. The study demonstrates the effectiveness of the Naïve Bayes algorithm for sentiment analysis in public health contexts, offering a novel approach to understanding public opinion on stunting. By leveraging social media data, this research provides real-time insights that can guide policy and public health interventions. Furthermore, the comparative analysis of algorithms provides valuable knowledge on the most effective models for sentiment analysis in complex, sentiment-driven public health issues. The study emphasizes the role of machine learning in addressing malnutrition and stunting through data-driven policy decisions.

CounselingQA: scaling professional mental-health guidance via dense retrieval and large language models

Results show that dense retrieval provides strong candidates and that transformer/LLM-driven reranking further elevates relevant, on-topic advice to the top positions, indicating that retrieval-first pipelines can help scale access to professional guidance.

AI-Based Assessment of Mental Health Status in College Students through Social Network Data Analysis

AI-based analysis of social network data provides an effective, non-invasive method for assessing college students' mental health status, offering significant potential for improving mental health monitoring and prevention strategies in educational settings.