D₂X: Depression Detection System Through X Using Hybrid Machine Learning

Currently, there is an increasing prevalence of depression among Thai people, often expressed through social media. Unfortunately, many individuals suffering from depression are unaware of their condition. This article introduces a depression detection system through $\mathbb {X}$ called $D_{2}\mathbb {X}$ , which utilizes sentiment analysis of $\mathbb {X}$ users’ tweets to predict their level of depression. The $D_{2}\mathbb {X}$ processes various types of tweets, including text messages, emoticons, and images, using a hybrid machine learning approach that combines support vector machine and random forest techniques. The study showed that combining text with emoticons resulted in the highest performance. Additionally, the research revealed that the most crucial feature for predicting levels of depression is the text tweets. Emoticon and image tweets were also found to enhance the effectiveness of detecting depression. The $D_{2}\mathbb {X}$ model, utilizing all types of tweet data, achieved the highest F-measure compared to other machine learning techniques. However, when using only the text messages from tweets, the $D_{2}\mathbb {X}$ model showed marginally lower performance than DistilBERT but outperformed other deep learning techniques. The $D_{2}\mathbb {X}$ also had the least model construction and usage time.