A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media

• URL: http://arxiv.org/abs/2511.20001v2
• Date: Mon, 01 Dec 2025 11:07:35 GMT
• Title: A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media
• Authors: Edward Ajayi, Martha Kachweka, Mawuli Deku, Emily Aiken,
• Abstract summary: Mental health challenges and cyberbullying are increasingly prevalent in digital spaces.<n>This paper introduces a unified multiclass classification framework for detecting ten distinct mental health and cyberbullying categories from social media data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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