Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier
DOI:
https://doi.org/10.52436/1.jutif.2026.7.2.5357Keywords:
Depression Detection, Extra Trees Classifier, Mental Health Informatics, PHQ-9 Labeling, Social Media Analytics, TikTok DataAbstract
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.
Downloads
References
World Health Organization, “Depressive disorder (depression),” https://www.who.int/news-room/fact-sheets/detail/depression. Accessed: Nov. 23, 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/depression
R. A. P. Sari and N. Yuliana, “Pola Komunikasi Orang Tua Pada Anak Generasi Z Terhadap Kesehatan Mental Anak,” vol. 2, pp. 2023–2054, Dec. 2023.
N. Muhamad, “Prevalensi Depresi di Indonesia Berdasarkan Kelompok Usia,” https://databoks.katadata.co.id/. Accessed: Jan. 11, 2025. [Online]. Available: https://databoks.katadata.co.id/
H. B. Alexander, “Ada 985 Kasus Bunuh Diri Remaja, Kesehatan Mental Penyebab Utama,” lestari.kompas.com. Accessed: Jan. 13, 2025. [Online]. Available: https://lestari.kompas.com/read/2023/12/17/162703186/ada-985-kasus-bunuh-diri-remaja-kesehatan-mental-penyebab-utama?page=all
F. Zulfikar, “Survei: 17,9 Juta Remaja Indonesia Punya Masalah Mental, Ini Gangguan yang Diderita,” detik.com. Accessed: Jan. 13, 2025. [Online]. Available: https://www.detik.com/edu/detikpedia/d-7150554/survei-17-9-juta-remaja-indonesia-punya-masalah-mental-ini-gangguan-yang-diderita?utm_source=chatgpt.com
B. Zaman et al., “Upaya Peningkatan Pengetahuan Masyarakat Tentang Pentingnya Kesehatan Jiwa,” Beujroh : Jurnal Pemberdayaan dan Pengabdian pada Masyarakat, vol. 2, no. 2, pp. 355–364, Aug. 2024, doi: 10.61579/beujroh.v2i2.185.
S. Kemp, “Digital 2024: Indonesia,” https://datareportal.com/. Accessed: Nov. 24, 2024. [Online]. Available: https://datareportal.com/reports/digital-2024-indonesia#:~:text=The%20state%20of%20digital%20in%20Indonesia%20in%202024&text=There%20were%20185.3%20million%20internet,percent%20of%20the%20total%20population.
A. A. Simanungkalit, W. Maharani, and P. H. Gani, “Depression Detection on Twitter Social Media Platform using Bidirectional Long-Short Term Memory,” JINAV: Journal of Information and Visualization, vol. 3, no. 2, pp. 190–203, Dec. 2022, doi: 10.35877/454RI.jinav1503.
A. Maududie, P. Armaini, and P. Pandunata, “Depression Early Warning System Based on Social Media Using Multinomial Naïve Bayes Algorithm,” 2024, pp. 62–71. doi: 10.2991/978-94-6463-445-7_8.
H. S. Alsagri and M. Ykhlef, “Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features,” IEICE Trans Inf Syst, vol. E103.D, no. 8, pp. 1825–1832, Aug. 2020, doi: 10.1587/transinf.2020EDP7023.
R. Chiong, G. S. Budhi, S. Dhakal, and F. Chiong, “A textual-based featuring approach for depression detection using machine learning classifiers and social media texts,” Comput Biol Med, vol. 135, p. 104499, Aug. 2021, doi: 10.1016/j.compbiomed.2021.104499.
S. Ghosh and T. Anwar, “Depression Intensity Estimation via Social Media: A Deep Learning Approach,” IEEE Trans Comput Soc Syst, vol. 8, no. 6, pp. 1465–1474, Dec. 2021, doi: 10.1109/TCSS.2021.3084154.
A. Amanat et al., “Deep Learning for Depression Detection from Textual Data,” Electronics (Basel), vol. 11, no. 5, p. 676, Feb. 2022, doi: 10.3390/electronics11050676.
M. R. Hidayatullah and W. Maharani, “Depression Detection on Twitter Social Media Using Decision Tree,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 4, pp. 677–683, Aug. 2022, doi: 10.29207/resti.v6i4.4275.
W.-L. Tey, H.-N. Goh, A. H.-L. Lim, and C.-K. Phang, “Pre- and Post-Depressive Detection using Deep Learning and Textual-based Features,” International Journal of Technology, vol. 14, no. 6, p. 1334, Oct. 2023, doi: 10.14716/ijtech.v14i6.6648.
V. Tejaswini, K. Sathya Babu, and B. Sahoo, “Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model,” ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 23, no. 1, pp. 1–20, Jan. 2024, doi: 10.1145/3569580.
E. Yoshua and W. Maharani, “Depression Detection of Users in Social-Media Twitter Using Decision Tree with Word2Vec,” Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi, vol. 9, no. 1, pp. 95–100, Feb. 2024, doi: 10.25139/inform.v9i1.7617.
M. Fadhel and W. Maharani, “Depression Detection of Users in Social Media X using IndoBERTweet,” Sinkron, vol. 8, no. 2, pp. 885–891, Mar. 2024, doi: 10.33395/sinkron.v9i2.13354.
Z. Yan, F. Peng, and D. Zhang, “DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content,” Decis Support Syst, vol. 191, p. 114421, 2025, doi: https://doi.org/10.1016/j.dss.2025.114421.
A. Qasim, G. Mehak, N. Hussain, A. Gelbukh, and G. Sidorov, “Detection of Depression Severity in Social Media Text Using Transformer-Based Models,” Information, vol. 16, no. 2, p. 114, Feb. 2025, doi: 10.3390/info16020114.
R. Chiong, G. S. Budhi, and E. Cambria, “Detecting Signs of Depression Using Social Media Texts through an Ensemble of Ensemble Classifiers,” IEEE Trans Affect Comput, pp. 1–17, 2025, doi: 10.1109/TAFFC.2025.3571749.
K. Kroenke, R. L. Spitzer, and J. B. W. Williams, “The PHQ-9: validity of a brief depression severity measure,” J Gen Intern Med, vol. 16, no. 9, pp. 606–613, Sep. 2001, doi: 10.1046/j.1525-1497.2001.016009606.x.
M. De Choudhury, M. Gamon, S. Counts, and E. Horvitz, “Predicting Depression via Social Media,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 7, no. 1, pp. 128–137, Aug. 2021, doi: 10.1609/icwsm.v7i1.14432.
M. G. Galety, C. Al Atroshi, B. K. Balabantaray, and S. N. Mohanty, Social Network Analysis: Theory and Applications. Scrivener Publishing, 2022.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, Jun. 2002, doi: 10.1613/jair.953.
Z.-Hua. Zhou, Introduction to ensemble methods. Chapman & Hall/CRC, 2012.
P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Mach Learn, vol. 63, no. 1, pp. 3–42, Apr. 2006, doi: 10.1007/s10994-006-6226-1.
M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf Process Manag, vol. 45, no. 4, pp. 427–437, 2009, doi: 10.1016/j.ipm.2009.03.002.
M. A. Moreno, N. Goniu, P. S. Moreno, and D. Diekema, “Ethics of social media research: common concerns and practical considerations.,” Cyberpsychol Behav Soc Netw, vol. 16, no. 9, pp. 708–13, Sep. 2013, doi: 10.1089/cyber.2012.0334.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Achmad Solichin, Helmi Zulqan, Painem, Anindya Putri Pradiptha
This work is licensed under a Creative Commons Attribution 4.0 International License.
