Bias in Machine Learning Models for Depression Detection

Table of Links Abstract and Introduction Methodology Results Discussion and Reference Abstract and Introduction Abstract and Introduction Methodology Methodology Results Results Discussion and Reference Discussion and Reference Discussion The escalating prevalence of mental health conditions, particularly depression, poses a significant global health challenge. Social media platforms have emerged as rich data sources where individuals express their thoughts and emotions, offering a unique opportunity to detect mental health issues through advanced computational methods. Machine learning and deep learning models hold promise for analyzing this vast, unstructured data to identify patterns indicative of depression. This systematic review aimed to evaluate the effectiveness of these models in detecting depression on social media, focusing on identifying and analyzing biases throughout the ML lifecycle. Summary of Key Findings Our review uncovered several key biases and methodological challenges that impact the reliability and generalizability of machine learning and deep learning models in this domain. Sampling biases emerged due to a predominant reliance on specific social media platforms, particularly Twitter, which was used in 63.8% of the studies. Additionally, most studies focused on English-language content and users from specific geographic regions, primarily the United States and Europe. These biases limit the representativeness of findings, as they do not capture the diversity of global social media users. In data preprocessing, many studies inadequately handled linguistic nuances, such as negations and sarcasm. Only about 23% of the studies explicitly addressed the handling of negative words or negations, which are crucial for accurate sentiment analysis in depression detection. Model development issues were also prominent. Inconsistent hyperparameter tuning practices were observed, with only 27.7% of the studies properly tuning hyperparameters for all models. Moreover, approximately 17% of the studies did not adequately partition their data into training, validation, and test sets. These practices can lead to overfitting, reducing the models’ ability to generalize to new data. Regarding model evaluation, many studies relied heavily on accuracy as the primary evaluation metric without addressing class imbalances inherent in depression detection datasets. While about 74.5% of the studies used metrics suitable for imbalanced data, such as precision, recall, F1 score, and AUROC, others did not, potentially skewing the evaluation of model performance. Finally, despite all studies including a limitations section, transparency varied significantly, with critical methodological details like data partitioning methods and hyperparameter settings often underreported. This inconsistency hinders reproducibility and the ability to fully assess the validity of the findings. Strengths and Limitations of the Review This systematic review stands out for its comprehensive scope, examining biases across the entire ML lifecycle, from sampling to reporting, in depression detection on social media. By not limiting the analysis to specific aspects, the review offers a holistic view of how biases can influence model validity. Another strength is the structured methodological approach, adhering to established guidelines with a well-defined search strategy and clear inclusion criteria. Focusing on studies published after 2010, it reflects the latest advancements in ML and DL applications for mental health. The use of established bias assessment tools, particularly PROBAST, adds rigor by systematically evaluating bias across key methodological domains. Additionally, the review’s detailed data extraction process facilitated a structured analysis, allowing for the identification of patterns and providing actionable recommendations, such as diversifying data sources and improving transparency. However, the review also has limitations. Limited database coverage and the English-only restriction may exclude valuable insights from non-English research, potentially affecting the generalizability of the findings. The focus on recent studies (post-2010) might have overlooked earlier influential works, while heterogeneity in study designs hindered direct comparisons and precluded a quantitative meta-analysis. Moreover, publication bias could skew findings toward positive results, and excluding grey literature means emerging methodologies may not be fully captured. Lastly, while ethical considerations were acknowledged, a deeper exploration of issues like data privacy and informed consent is warranted. These limitations suggest areas for improvement in future research, such as broadening database and language coverage, including grey literature, and conducting a meta-analysis where feasible. By addressing these areas, future studies can enhance the robustness of ML models for mental health detection and provide a more comprehensive, ethical, and globally relevant understanding of the field. Implications for Future Research To enhance the generalizability and applicability of machine learning and deep learning models in depression detection on social media, addressing identified biases is essential. First, diversifying data sources across multiple social media platforms and incorporating non-English languages and underrepresented regions will improve representativeness and generalizability.. Improving sampling methods is crucial. Combining keyword-based sampling with random sampling techniques can help reduce selection bias and capture users who may not explicitly mention depression but exhibit relevant behaviors. In the data preprocessing step, researchers should standardize practices to explicitly handle linguistic nuances like negations and sarcasm, which are vital for accurate sentiment analysis. Additionally, applying resampling or reweighting techniques can help balance datasets, ensuring that both classes—particularly the minority depressive class—are adequately represented. Advanced natural language processing techniques that account for linguistic nuances, such as sarcasm and context-dependent meanings, should be employed. Consistent and comprehensive hyperparameter tuning across all models is essential to ensure fair comparisons and optimize model performance. Proper data partitioning practices, including the use of validation and test sets, should be implemented to prevent overfitting and assess model generalizability. When evaluating models, researchers should prioritize metrics that account for class imbalance, such as precision, recall, F1 score, and AUROC. These metrics provide a more balanced assessment of model performance and are more informative in the context of detecting depression, where the minority class is of primary interest. Concluding Remarks This systematic review highlights significant methodological limitations in current research on detecting depression through social media analysis using machine learning and deep learning models. Addressing these limitations is critical to developing more accurate, reliable, and generalizable models that can effectively identify individuals at risk of depression. Future research should focus on diversifying data sources, improving sampling methods, enhancing data preprocessing and model development practices, and employing appropriate evaluation metrics to ensure balanced and meaningful assessments. By advancing these methodological approaches, researchers can contribute to the advancement of mental health detection tools that are ethically sound and effective across diverse populations and platforms. Such advancements hold the potential to facilitate early intervention strategies, ultimately improving mental health outcomes on a global scale. Appendix 1. Reviewed Studies on Machine Learning Models for Depression Detection on Social Media Reference: Agarwal, A. K., Mittal, J., Tran, A., Merchant, R., & Guntuku, S. C. (2023). Investigating Social Media to Evaluate Emergency Medicine Physicians' Emotional Well-being During COVID19. JAMA Netw Open, 6(5), e2312708. https://doi.org/10.1001/jamanetworkopen.2023.12708 Angskun, J., Tipprasert, S., & Angskun, T. (2022). Big data analytics on social networks for real-time depression detection. J Big Data, 9(1), 69. https://doi.org/10.1186/s40537-022- 00622-2 Baghdadi, N. A., Malki, A., Magdy Balaha, H., AbdulAzeem, Y., Badawy, M., & Elhosseini, M. (2022). An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Comput Sci, 8, e1070. https://doi.org/10.7717/peerj-cs.1070 Baird, A., Xia, Y., & Cheng, Y. (2022). Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis. JAMIA Open, 5(2), ooac028. https://doi.org/10.1093/jamiaopen/ooac028 Beierle, F., Pryss, R., & Aizawa, A. (2023). Sentiments about Mental Health on TwitterBefore and during the COVID-19 Pandemic. Healthcare (Basel), 11(21). https://doi.org/10.3390/healthcare11212893 Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Publisher description http://www.loc.gov/catdir/enhancements/fy0818/2006922522-d.html Borba de Souza, V., Campos Nobre, J., & Becker, K. (2022). DAC Stacking: A Deep Learning Ensemble to Classify Anxiety, Depression, and Their Comorbidity From Reddit Texts. IEEE J Biomed Health Inform, 26(7), 3303-3311. https://doi.org/10.1109/JBHI.2022.3151589 Calvo, R. A., Milne, D. N., Hussain, M. S., & Christensen, H. (2017). Natural language processing in mental health applications using non-clinical texts. Natural Language Engineering, 23(5), 649-685. https://doi.org/10.1017/S1351324916000383 Chandler, J., & Shapiro, D. (2016). Conducting Clinical Research Using Crowdsourced Convenience Samples. Annu Rev Clin Psychol, 12, 53-81. https://doi.org/10.1146/annurevclinpsy-021815-093623 Chandra, R., & Krishna, A. (2021). COVID-19 sentiment analysis via deep learning during the rise of novel cases. PLoS One, 16(8), e0255615. https://doi.org/10.1371/journal.pone.0255615 Chawla, N., Japkowicz, N., & Kołcz, A. (2004). Editorial: Special Issue on Learning from Imbalanced Data Sets. SIGKDD Explorations, 6, 1-6. https://doi.org/10.1145/1007730.1007733 Chen, L., Gong, T., Kosinski, M., Stillwell, D., & Davidson, R. L. (2017). Building a profile of subjective well-being for social media users. PLoS One, 12(11), e0187278. https://doi.org/10.1371/journal.pone.0187278 Chiong, R., Budhi, G. S., Dhakal, S., & Chiong, F. (2021). A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Comput Biol Med, 135, 104499. https://doi.org/10.1016/j.compbiomed.2021.104499 Claesen, M., & De Moor, B. (2015). Hyperparameter Search in Machine Learning. Danet, B., & Herring, S. C. (2007). The multilingual Internet: language, culture, and communication online. Oxford University Press. Das Swain, V., Ye, J., Ramesh, S. K., Mondal, A., Abowd, G. D., & De Choudhury, M. (2024). Leveraging Social Media to Predict COVID-19-Induced Disruptions to Mental WellBeing Among University Students: Modeling Study. JMIR Form Res, 8, e52316. https://doi.org/10.2196/52316 Davis, B. D., McKnight, D. E., Teodorescu, D., Quan-Haase, A., Chunara, R., Fyshe, A., & Lizotte, D. J. (2020). Quantifying depression-related language on social media during the COVID-19 pandemic. Int J Popul Data Sci, 5(4), 1716. https://doi.org/10.23889/ijpds.v5i4.1716 de Anta, L., Alvarez-Mon, M. A., Ortega, M. A., Salazar, C., Donat-Vargas, C., SantomaVilaclara, J., Martin-Martinez, M., Lahera, G., Gutierrez-Rojas, L., Rodriguez-Jimenez, R., Quintero, J., & Alvarez-Mon, M. (2022). Areas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study. J Pers Med, 12(2). https://doi.org/10.3390/jpm12020155 De Choudhury, M., Counts, S., & Horvitz, E. (2013). Social media as a measurement tool of depression in populations. Proceedings of the 5th Annual ACM Web Science Conference, De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. International AAAI Conference on Web and Social Media, Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805 Doan, S., Ritchart, A., Perry, N., Chaparro, J. D., & Conway, M. (2017). How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health Surveill, 3(2), e35. https://doi.org/10.2196/publichealth.5939 Fernández, A., García, S., Galar, M., Prati, R., Krawczyk, B., & Herrera, F. (2018). Learning from Imbalanced Data Sets. https://doi.org/10.1007/978-3-319-98074-4 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press. Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., Polsky, D., Volpp, K. G., & Merchant, R. (2019). Studying expressions of loneliness in individuals using twitter: an observational study. BMJ Open, 9(11), e030355. https://doi.org/10.1136/bmjopen2019-030355 Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Psychology, 18, 43-49. https://doi.org/10.1016/j.copsyc.2017.07.005 Hargittai, E. (2015). Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites. The Annals of the American Academy of Political and Social Science, 659, 63-76. http://www.jstor.org/stable/24541849 Hastie, T., Friedman, J. H., & Tibshirani, R. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer. He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284. https://doi.org/10.1109/TKDE.2008.239 Helmy, A., Nassar, R., & Ramdan, N. (2024). Depression detection for twitter users using sentiment analysis in English and Arabic tweets. Artif Intell Med, 147, 102716. https://doi.org/10.1016/j.artmed.2023.102716 Islam, M. R., Kabir, M. A., Ahmed, A., Kamal, A. R. M., Wang, H., & Ulhaq, A. (2018). Depression detection from social network data using machine learning techniques. Health Inf Sci Syst, 6(1), 8. https://doi.org/10.1007/s13755-018-0046-0 Jamali, A. A., Berger, C., & Spiteri, R. J. (2023). Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach. JMIR AI, 2, e49531. https://doi.org/10.2196/49531 Japkowicz, N. (2013). Assessment Metrics for Imbalanced Learning. The Institute of Electrical and Electronics Engineers, Inc. https://doi.org/10.1002/9781118646106 Japkowicz, N., & Stephen, S. (2002). The Class Imbalance Problem: A Systematic Study. Intell. Data Anal., 6, 429-449. https://doi.org/10.3233/IDA-2002-6504 Johnson, K. I., Andersson, G., & Carlbring, P. (2019). Using the Internet to Address Psychological Problems: The Good, the Bad, and the Ugly. International Journal of Environmental Research and Public Health, 16(23), 4593. https://doi.org/10.3390/ijerph16234593 Kaur, H., Ahsaan, S. U., Alankar, B., & Chang, V. (2021). A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Inf Syst Front, 23(6), 1417-1429. https://doi.org/10.1007/s10796-021-10135-7 Kavuluru, R., Williams, A. G., Ramos-Morales, M., Haye, L., Holaday, T., & Cerel, J. (2016). Classification of Helpful Comments on Online Suicide Watch Forums. ACM BCB, 2016, 32-40. https://doi.org/10.1145/2975167.2975170 Kelley, S. W., Mhaonaigh, C. N., Burke, L., Whelan, R., & Gillan, C. M. (2022). Machine learning of language use on Twitter reveals weak and non-specific predictions. NPJ Digit Med, 5(1), 35. https://doi.org/10.1038/s41746-022-00576-y Kessler, R. C. B., P.; Demler, O.; Jin, R.; Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62(6), 593-602. https://doi.org/10.1001/archpsyc.62.6.593 Khandelwal, A., & Sawant, S. (2020). NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution. Proceedings of the Twelfth Language Resources and Evaluation Conference (LREC 2020), Kim, J., Lee, J., Park, E., & Han, J. (2020). A deep learning model for detecting mental illness from user content on social media. Sci Rep, 10(1), 11846. https://doi.org/10.1038/s41598- 020-68764-y Levanti, D., Monastero, R. N., Zamani, M., Eichstaedt, J. C., Giorgi, S., Schwartz, H. A., & Meliker, J. R. (2023). Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities. AJPM Focus, 2(1), 100062. https://doi.org/10.1016/j.focus.2022.100062 Li, Y., Cai, M., Qin, S., & Lu, X. (2020). Depressive Emotion Detection and Behavior Analysis of Men Who Have Sex With Men via Social Media. Front Psychiatry, 11, 830. https://doi.org/10.3389/fpsyt.2020.00830 Low, D. M., Rumker, L., Talkar, T., Torous, J., Cecchi, G., & Ghosh, S. S. (2020). Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study. J Med Internet Res, 22(10), e22635. https://doi.org/10.2196/22635 Morstatter, F. P., Jürgen; Liu, Huan; Carley, Kathleen M. (2013). Is the sample good enough? Comparing data from twitter's streaming API with Twitter's firehose 7th International AAAI Conference on Weblogs and Social Media, ICWSM 2013, http://www.scopus.com/inward/record.url?scp=84892704954&partnerID=8YFLogxK Ng, A. (2018). Machine Learning Yearning. https://info.deeplearning.ai/machine-learningyearning-book Ng, Q. X., Lim, Y. L., Ong, C., New, S., Fam, J., & Liew, T. M. (2024). Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023. Front Psychiatry, 15, 1369727. https://doi.org/10.3389/fpsyt.2024.1369727 Obagbuwa, I. C., Danster, S., & Chibaya, O. C. (2023). Supervised machine learning models for depression sentiment analysis. Front Artif Intell, 6, 1230649. https://doi.org/10.3389/frai.2023.1230649 Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Front Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013 Ophir, Y., Tikochinski, R., Asterhan, C. S. C., Sisso, I., & Reichart, R. (2020). Deep neural networks detect suicide risk from textual facebook posts. Sci Rep, 10(1), 16685. https://doi.org/10.1038/s41598-020-73917-0 Organization, W. H. (2020). Depression https://www.who.int/news-room/factsheets/detail/depression Owen, D., Antypas, D., Hassoulas, A., Pardinas, A. F., Espinosa-Anke, L., & Collados, J. C. (2023). Enabling Early Health Care Intervention by Detecting Depression in Users of WebBased Forums using Language Models: Longitudinal Analysis and Evaluation. JMIR AI, 2, e41205. https://doi.org/10.2196/41205 Owen, D., Antypas, D., Hassoulas, A., Pardinas, A. F., Espinosa-Anke, L., & Collados, J. C. (2023). Enabling Early Health Care Intervention by Detecting Depression in Users of WebBased Forums using Language Models: Longitudinal Analysis and Evaluation. JMIR AI, 2, e41205. https://doi.org/10.2196/41205 with realtime data and machine learning. Npj Ment Health Res, 3(1), 3. https://doi.org/10.1038/s44184-023-00045-8 Prieto, V. M., Matos, S., Alvarez, M., Cacheda, F., & Oliveira, J. L. (2014). Twitter: a good place to detect health conditions. PLoS One, 9(1), e86191. https://doi.org/10.1371/journal.pone.0086191 Probst, P., Boulesteix, A.-L., & Bischl, B. (2019). Tunability: Importance of hyperparameters of machine learning algorithms. Journal of Machine Learning Research, 20(53), 1-32. Roy, A., Nikolitch, K., McGinn, R., Jinah, S., Klement, W., & Kaminsky, Z. A. (2020). A machine learning approach predicts future risk to suicidal ideation from social media data. NPJ Digit Med, 3, 78. https://doi.org/10.1038/s41746-020-0287-6 Rude, S., Gortner, E.-M., & Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8), 1121-1133. https://doi.org/10.1080/02699930441000030 Saha, K., Yousuf, A., Boyd, R. L., Pennebaker, J. W., & De Choudhury, M. (2022). Social Media Discussions Predict Mental Health Consultations on College Campuses. Sci Rep, 12(1), 123. https://doi.org/10.1038/s41598-021-03423-4 Shatte, A. B. R., Hutchinson, D. M., Fuller-Tyszkiewicz, M., & Teague, S. J. (2020). Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach. Cyberpsychol Behav Soc Netw, 23(9), 611-618. https://doi.org/10.1089/cyber.2019.0746 Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426-1448. https://doi.org/10.1017/S0033291719000151 Singh, A., & Singh, J. (2022). Synthesis of Affective Expressions and Artificial Intelligence to Discover Mental Distress in Online Community. Int J Ment Health Addict, 1-26. https://doi.org/10.1007/s11469-022-00966-z Sun, B., Zhang, Y., He, J., Xiao, Y., & Xiao, R. (2019). An automatic diagnostic network using skew-robust adversarial discriminative domain adaptation to evaluate the severity of depression. Comput Methods Programs Biomed, 173, 185-195. https://doi.org/10.1016/j.cmpb.2019.01.006 pnarekha, H., Nayak, J., Behera, H. S., Dash, P. B., & Pelusi, D. (2023). An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets. Math Biosci Eng, 20(2), 2382-2407. https://doi.org/10.3934/mbe.2023112 Thorstad, R., & Wolff, P. (2019). Predicting future mental illness from social media: A big-data approach. Behav Res Methods, 51(4), 1586-1600. https://doi.org/10.3758/s13428-019- 01235-z Trifan, A., Semeraro, D., Drake, J., Bukowski, R., & Oliveira, J. L. (2020). Social Media Mining for Postpartum Depression Prediction. Stud Health Technol Inform, 270, 1391-1392. https://doi.org/10.3233/SHTI200457 Ueda, M., Watanabe, K., & Sueki, H. (2023). Correction: Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm. J Med Internet Res, 25, e47549. https://doi.org/10.2196/47549 Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & Group, P. (2019). PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of internal medicine, 170(1), 51– 58. https://doi.org/https://doi.org/10.7326/M18-1376 Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & Group, P. (2019). PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of internal medicine, 170(1), 51– 58. https://doi.org/https://doi.org/10.7326/M18-1376 Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2021). Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study. JMIR Ment Health, 8(8), e19824. https://doi.org/10.2196/19824 Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295-316. https://doi.org/https://doi.org/10.1016/j.neucom.2020.07.061 Yao, H., Rashidian, S., Dong, X., Duanmu, H., Rosenthal, R. N., & Wang, F. (2020). Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach. J Med Internet Res, 22(11), e15293. https://doi.org/10.2196/15293 Yazdavar, A. H., Al-Olimat, H. S., Ebrahimi, M., Bajaj, G., Banerjee, T., Thirunarayan, K., Pathak, J., & Sheth, A. (2017). Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. Proc IEEE ACM Int Conf Adv Soc Netw Anal Min, 2017, 1191-1198. https://doi.org/10.1145/3110025.3123028 Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Romine, W., Sheth, A., Monadjemi, A. H., Thirunarayan, K., Meddar, J. M., Myers, A., Pathak, J., & Hitzler, P. (2020). Multimodal mental health analysis in social media. PLoS One, 15(4). https://doi.org/10.1371/journal.pone.0226248 Yin, Z., Fabbri, D., Rosenbloom, S. T., & Malin, B. (2015). A Scalable Framework to Detect Personal Health Mentions on Twitter. J Med Internet Res, 17(6), e138. https://doi.org/10.2196/jmir.4305 Zhou, T. H., Hu, G. L., & Wang, L. (2019). Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks. Int J Environ Res Public Health, 16(6). https://doi.org/10.3390/ijerph16060953 Zogan, H., Razzak, I., Wang, X., Jameel, S., & Xu, G. (2022). Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media. World Wide Web, 25(1), 281-304. https://doi.org/10.1007/s11280-021-00992-2 Authors: (1) Yuchen Cao, Khoury college of computer science, Northeastern University; (2) Jianglai Dai, Department of EECS, University of California, Berkeley; (3) Zhongyan Wang, Center for Data Science, New York University; (4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania; (5) Xiaorui Shen, Khoury college of computer science, Northeastern University; (6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania; (7) Yexin Tian, Georgia Institute of Technology, College of Computing. Authors: Authors: (1) Yuchen Cao, Khoury college of computer science, Northeastern University; (2) Jianglai Dai, Department of EECS, University of California, Berkeley; (3) Zhongyan Wang, Center for Data Science, New York University; (4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania; (5) Xiaorui Shen, Khoury college of computer science, Northeastern University; (6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania; (7) Yexin Tian, Georgia Institute of Technology, College of Computing. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv