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

TL;DR

This paper develops a multiclass social media detection system for mental health and cyberbullying, demonstrating the effectiveness of domain-adapted transformers like MentalBERT with high accuracy and explainability features.

Contribution

It introduces a unified classification framework with a novel balanced data pipeline, evaluates multiple models including fine-tuned transformers, and presents a practical explainability tool for moderation workflows.

Findings

End-to-end fine-tuning improves detection performance.

MentalBERT outperforms generic models and zero-shot baselines.

The system achieves 0.92 accuracy and 0.76 Macro F1 score.

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.