Comparative analysis for mental health prediction tasks based on social media posts

Abstract

In this thesis, titled "Comparative Analysis for Mental Health Prediction Tasks Based on Social Media Posts," the challenge of detecting depression and suicidal ideation through natural language processing (NLP) on social media platforms is addressed. Depression, a prevalent and debilitating mental health disorder characterized by persistent feelings of sadness, hopelessness, and loss of interest, affects millions globally. The study aims to enhance early detection and intervention strategies by leveraging advanced machine learning models. Social media's widespread use presents a unique opportunity to analyze user-generated content for mental health insights, providing a non-intrusive method to monitor and support individuals at risk. Social media platforms like Twitter and Reddit offer rich, real-time data sources where individuals often express their emotions and mental states, making them valuable for detecting signs of mental health issues. The research utilizes datasets from these platforms, focusing on depressive and suicidal content. Data preprocessing steps include tokenization, lemmatization, and feature extraction using techniques such as TF-IDF and word embeddings. Various machine learning models, including Logistic Regression, Random Forest, and state-of-the-art transformer-based models like BERT and DistilBERT, are fine-tuned for the task. Comparative analysis between these models highlights their respective strengths and weaknesses in detecting depressive and suicidal language. The evaluation reveals that transformer-based models, particularly DistilBERT, significantly outperform traditional machine learning methods in accuracy, precision, recall, and F1-score. For instance, DistilBERT achieved an F1-score of 0.99 on the Depression Twitter dataset, highlighting its capability to discern depressive content with high precision and recall. Comparatively, the Random Forest classifier also showed strong performance but was slightly outpaced by the transformer models. This comparative analysis provides valuable insights into the effectiveness of different machine learning approaches for mental health prediction. The thesis underscores the need for further research into the integration of multimodal data, including textual and non-textual inputs such as images and user interaction patterns. Future studies should focus on the ethical considerations of using social media data, ensuring user privacy and consent. Additionally, continuous model adaptation and fine-tuning to evolving linguistic trends and emerging data sources will be crucial for maintaining model accuracy and relevance. Addressing these ethical and technical challenges is essential for the responsible deployment of these technologies in real-world mental health support systems. In conclusion, this research demonstrates the potential of NLP and machine learning in monitoring and supporting mental health through social media analysis. By harnessing the vast and diverse data available on social media platforms, we can develop proactive measures to identify and assist individuals at risk of depression and suicidal ideation. The findings advocate for the integration of these technologies into mental health services to provide timely and accurate interventions, ultimately contributing to improved mental health outcomes. This approach represents a significant step forward in the ongoing effort to leverage digital technologies for better mental health care.