Utilizing Deep Learning Techniques for Mental Disorder Prediction and Support Using Reddit Data

The increasing prevalence of mental health disorders necessitates innovative approaches to early detection and intervention. Social media platforms like Reddit provide a rich source of textual data that can be leveraged to identify mental health issues based on user-generated content. This study aims to classify mental health disorders by analyzing Reddit comments. The dataset used is Reddit SuicideWatch and Mental Health Collection that includes 54,412 posts, which are classified into several mental health disorders. After that, preprocessing steps were done, which includes tokenization, padding, word embeddings, and handling imbalance data. Then, a Custom-CNN (Convolutional Neural Network) and a Custom- RNN (Recurrent Neural Network) were used to classify the data. These evaluation metrics were then used to evaluate the models: accuracy, precision, recall, F1-score, and ROC AUC Score. Notable results are that the RNN has a higher accuracy of 0.65 compared to 0.64 by CNN. In addition, both RNN and CNN also have high ROC AUC scores of 0.88 and 0.87 respectively. In conclusion, the results show that the models are good at differentiating one class from another, however, both models have problems accurately classifying the correct mental health disorder from the texts. This shows promise as the models can definitely be improved with better and more balanced data, and exploration on LLMs (Large Language Models) can also be valuable for this study and issue. In addition to that, those improvements can be applied to the act of intervention by integrating it to technology such as a conversational AI or wearable actuators that have the ability to predict and prevent through real-time monitoring.

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