Leveraging Social Media Signals for Depression Prediction: Insights from Twitter

Depression, a mental health state that has been rising exponentially causing harm to individuals and exerting a detrimental impact on society [13]. This article explores how the Naive Bayes algorithm, a probabilistic machine learning model, can be used for predicting depression, specifically using Twitter posts [9]. Renowned for its effectiveness in text classification tasks, Naïve Bayes is implemented in this research to help detect verbal patterns that might suggest depressive behavior [6]. The model training has been conducted on a dataset of labeled tweets categorized as either indicative of depression or not, which allows it to learn and recognize patterns associated with depressive behavior. The results prove the algorithm classifies tweets well, proving to be a practical and efficient method for digital mental health screening [9], [13]. By using publicly available social media data, this work showcases the potential for creating affordable and scalable tools for early detection, which can support timely intervention and promote better mental health outcomes [3], [5].