Mental Health Detection Using Social Media Data

Millions of people share their feelings, thoughts, and struggles daily on platforms like Twitter, Reddit, and Facebook. Subtle language changes in posts can often indicate underlying mental health issues such as depression, anxiety, or loneliness. Detecting these early through natural language analysis can enable timely support, interventions, and awareness. However, mental health signals are often hidden in informal, slang-heavy, emotionally complex text data, making machine learning-based approaches essential for accurate analysis.

Using anonymized datasets of social media posts labeled with mental health categories, machine learning models can be trained to detect emotional states. Sentiment analysis, emotion classification (sadness, anger, fear, joy), and stress detection can all be implemented using techniques like TF-IDF+Logistic Regression, LSTM networks, or transformer models like RoBERTa or DistilBERT fine-tuned on mental health corpora. Predictive insights can help researchers and NGOs monitor trends and support mental wellness campaigns more effectively.

Start by collecting anonymized social media posts from platforms like Reddit, Twitter, or public datasets like CLPsych or eRisk. Preprocessing steps involve tokenization, cleaning slang/abbreviations, emoji handling, and stopword removal. Sentiment analysis models and emotion classifiers are trained to detect depression, anxiety, and related mental health conditions. Evaluation uses F1-score, precision, recall, and confusion matrices to measure how accurately emotions and mental health risks are classified.

• Collect or access social media datasets labeled for depression, anxiety, stress, or emotional states.
• Preprocess the text data: expand slang, remove noise, handle emojis, and standardize informal language patterns.
• Train models like Logistic Regression, LSTM, RoBERTa, or DistilBERT to classify emotional states and mental health indicators.
• Evaluate model performance with sensitivity-focused metrics to prioritize correct identification of at-risk individuals.
• Deploy an ethical, privacy-preserving system that visualizes mental health trends without exposing individuals' private information.

• CLPsych Shared Task Mental Health Dataset
• eRisk Mental Health Challenge Datasets
• Hugging Face Transformer Models