Jul 2026· Social Network Analysis and Mining· 0 citations
TL;DR
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.
Abstract
In the digital era, the internet and social media have emerged as essential platforms for individuals facing mental health issues, often used for seeking information and community support. Despite the resources of informal advice available on social media, the complexity of these issues frequently exceeds non-expert knowledge. Specialized sites such as CounselChat and 7Cups offer professional guidance, yet many at-risk individuals still rely on unmoderated sources and general web search. We address this gap by investigating ranking strategies that match pre-existing expert advice to incoming mental-health questions. We introduce , a collection built from two specialized websites, pairing user questions with verified expert responses. We address the task as answer retrieval (AR): given a question, rank expert answers by relevance. We evaluate dense retrieval with SentenceBERT and MentalBERT, and propose a second stage that improves the initial ranking via transformer-based models and large language models (LLMs), used for filtering non-relevant candidates and for reordering. Beyond retrieval, we analyze linguistic style and affective attributes across topics, questions, and responses. Results show that dense retrieval provides strong candidates and that transformer/LLM-driven reranking further elevates relevant, on-topic advice to the top positions. We further conduct qualitative error analyses, including human evaluation to study the benefits and limitations of our approaches. Taken together, these findings indicate that retrieval-first pipelines can help scale access to professional guidance.
Mental illnesses like depression, anxiety, stress, and so on have become more widespread, and this has necessitated the availability of assessment tools that are readily available, scalable, and automated. The paper will offer a web-based mental health risk assessment system that utilizes the latest technology of Natural Language Processing (NLP) in real-time to analyze textual information provided by users. The offered system incorporates a hybrid deep learning framework with the relation of DeBERTa, BiLSTM, and XGBoost to promote the contextual comprehension, sequential emotional pattern identification, and effective classification of the performance. First, text input by the users is received with a secure web interface and processed with general NLP preprocessing methods, such as tokenization, lemmatization, and sentiment normalization. To extract deep semantic relationships in the text, DeBERTa is used to extract contextual embeddings. Such embeddings are also trained in the form of a Bidirectional Long Short-Term Memory (BiLSTM) network in order to capture emotional dynamics and linguistic reinforcing relations. Fused feature representation, sentiments, and linguistic indicators are input into an XGBoost classifier to predict mental health in multi-class. There is a weighted risk scoring system used to measure the level of severity and provide tailored feedback. It is experimentally tested on standard mental health text data sets that the proposed hybrid framework is more effective than the traditional machine learning and standalone transformer models in terms of accuracy, precision, recall, and F1-score. The architecture is scalable to the deployment of a web system, which is guaranteed to perform in real-time, secure data, and privacy of users. The suggested framework offers a solid and smart instrument to identify the risk of mental health early and help intervene in time and to promote the development of digital health care.
D. D, S. S, L. K et al.· 2026 4th International Confe...· 0 citations
An ontology-guided framework that integrates a Knowledge Graph, an Ontology-Informed Retrieval Classifier, and a Large Language Model for interpretable mental health detection from social media text demonstrates that the KG–ORC cross-validation gate measurably improves predictive reliability over single component baselines, and that ontology-guided, knowledge-grounded reasoning offers a principled path toward interpretable and knowledge-consistent mental health analysis from social media.
Amina Tahir, Ghulam Mustafa, Muhammad Tanvir Afzal et al.· Social Network Analysis and...· 0 citations
This work extends PubHealthBench, a question answering benchmark of 7,929 questions derived from UK Government public health guidance, into a retrieval-augmented setting and systematically evaluates retrieval and generation choices, and introduces a rubric-based LLM-as-a-judge covering faithfulness, completeness, clarity, and factual consistency.
Felix Feldman, Joshua Harris, Timothy Laurence et al.· 0 citations
Overall, LLMs have the potential to complement peer responses in OHCs, but require greater emotional depth, reasoning transparency, and alignment with community norms.
M. Hussein, R. Doshi, L. He et al.· medRxiv· 0 citations
HealthMate is presented, an intelligent, explainable AI chatbot framework designed for preliminary healthcare consultation that demonstrates rapid retrieval, robust natural language comprehension, and clear explainability without replacing professional medical diagnosis.
K. Jyothi, Shaik Khasim Basha· International Scientific Jou...· 0 citations
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