{"ID":2836792,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20001","arxiv_id":"2511.20001","title":"A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media","abstract":"Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten distinct mental health and cyberbullying categories from social media data. We curate datasets from Twitter and Reddit, implementing a rigorous \"split-then-balance\" pipeline to train on balanced data while evaluating on a realistic, held-out imbalanced test set. We conducted a comprehensive evaluation comparing traditional lexical models, hybrid approaches, and several end-to-end fine-tuned transformers. Our results demonstrate that end-to-end fine-tuning is critical for performance, with the domain-adapted MentalBERT emerging as the top model, achieving an accuracy of 0.92 and a Macro F1 score of 0.76, surpassing both its generic counterpart and a zero-shot LLM baseline. Grounded in a comprehensive ethical analysis, we frame the system as a human-in-the-loop screening aid, not a diagnostic tool. To support this, we introduce a hybrid SHAPLLM explainability framework and present a prototype dashboard (\"Social Media Screener\") designed to integrate model predictions and their explanations into a practical workflow for moderators. Our work provides a robust baseline, highlighting future needs for multi-label, clinically-validated datasets at the critical intersection of online safety and computational mental health.","short_abstract":"Mental health challenges and cyberbullying are increasingly prevalent in digital spaces, necessitating scalable and interpretable detection systems. This paper introduces a unified multiclass classification framework for detecting ten distinct mental health and cyberbullying categories from social media data. We curate...","url_abs":"https://arxiv.org/abs/2511.20001","url_pdf":"https://arxiv.org/pdf/2511.20001v4","authors":"[\"Edward Ajayi\",\"Martha Kachweka\",\"Mawuli Deku\",\"Emily Aiken\"]","published":"2025-11-25T07:12:09Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SI\"]","methods":"[\"Transformer\",\"Large Language Model\"]","has_code":false}