<![CDATA[Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.]]>
