Published on in Vol 9, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27244, first published .
Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review

Journals

  1. Baghdadi N, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Computer Science 2022;8:e1070 View
  2. Kesler S, Henneghan A, Thurman W, Rao V. Identifying Themes for Assessing Cancer-Related Cognitive Impairment: Topic Modeling and Qualitative Content Analysis of Public Online Comments. JMIR Cancer 2022;8(2):e34828 View
  3. Pan W, Wang X, Zhou W, Hang B, Guo L. Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches. International Journal of Environmental Research and Public Health 2023;20(3):2688 View
  4. Bhattacharya M, Roy S, Chattopadhyay S, Das A, Shetty S. A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges. SECURITY AND PRIVACY 2023;6(1) View
  5. Barua P, Vicnesh J, Lih O, Palmer E, Yamakawa T, Kobayashi M, Acharya U. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cognitive Neurodynamics 2024;18(1):1 View
  6. Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing 2022;130:109713 View
  7. Lyu S, Ren X, Du Y, Zhao N. Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons. Frontiers in Psychiatry 2023;14 View
  8. Yao S, Wang F, Chen J, Lu Q. Utilizing health-related text on social media for depression research: themes and methods. Library Hi Tech 2025;43(1):274 View
  9. Akyol S. New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks. PeerJ Computer Science 2023;9:e1661 View
  10. Chen J, Hu Y, Lai Q, Wang W, Chen J, Liu H, Srivastava G, Bashir A, Hu X. IIFDD: Intra and inter-modal fusion for depression detection with multi-modal information from Internet of Medical Things. Information Fusion 2024;102:102017 View
  11. Thushari P, Aggarwal N, Vajrobol V, Saxena G, Singh S, Pundir A. Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions. Social Network Analysis and Mining 2023;13(1) View
  12. Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. npj Mental Health Research 2023;2(1) View
  13. Counts N, Bloom D, Halfon N. Psychological distress as a systemic economic risk in the USA. Nature Mental Health 2023;1(12):950 View
  14. Liu Y. Depression detection via a Chinese social media platform: a novel causal relation-aware deep learning approach. The Journal of Supercomputing 2024;80(8):10327 View
  15. Tudehope L, Harris N, Vorage L, Sofija E. What methods are used to examine representation of mental ill-health on social media? A systematic review. BMC Psychology 2024;12(1) View
  16. Khan A, Ali R. Unraveling minds in the digital era: a review on mapping mental health disorders through machine learning techniques using online social media. Social Network Analysis and Mining 2024;14(1) View
  17. Gan L, Guo Y, Yang T. Machine Learning for Depression Detection on Web and Social Media. International Journal on Semantic Web and Information Systems 2024;20(1):1 View
  18. Yuan Y, Kasson E, Taylor J, Cavazos-Rehg P, De Choudhury M, Aledavood T. Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach. JMIR Formative Research 2024;8:e54433 View
  19. Prof. Saba Anjum Patel , Kalakshi Jadhav , Sayali Ligade , Vishal Mahajan , Keshav Anant . Depression Prediction using Machine Learning Algorithms. International Journal of Advanced Research in Science, Communication and Technology 2024:526 View
  20. Abbasi A, Parsons J, Pant G, Sheng O, Sarker S. Pathways for Design Research on Artificial Intelligence. Information Systems Research 2024;35(2):441 View
  21. Pandey M, Litoriya R, Pandey P, Panigrahi P, Gupta B. Trapezoidal fuzzy number methodology for prioritising the predictors of social media addiction. Enterprise Information Systems 2024;18(9) View
  22. Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. Journal of Affective Disorders 2024;361:445 View
  23. Wang L, Zhang Y, Zhou B, Cao S, Hu K, Tan Y. Automatic depression prediction via cross-modal attention-based multi-modal fusion in social networks. Computers and Electrical Engineering 2024;118:109413 View
  24. Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
  25. Qin R, Yang K, Abbasi A, Dobolyi D, Seyedi S, Griner E, Kwon H, Cotes R, Jiang Z, Clifford G, Cook R. Language Models for Online Depression Detection: A Review and Benchmark Analysis on Remote Interviews. ACM Transactions on Management Information Systems 2025;16(2):1 View
  26. Pourahmad Ghalejough A, Abbasi Avval S, Haghparast F, Gharehbaglou M. Star architecture in online public discourse: exploring Reddit user-generated content on the Vessel, New York, through a text analytics approach. Archnet-IJAR: International Journal of Architectural Research 2025;19(2):372 View
  27. Ogunleye B, Sharma H, Shobayo O. Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection. Big Data and Cognitive Computing 2024;8(9):112 View
  28. Edler J, Winter M, Steinmetz H, Cohrdes C, Baumeister H, Pryss R. Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study. Interactive Journal of Medical Research 2024;13:e53248 View
  29. Deng F, Cao Y, Wang H, Zhao S. Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study. Journal of Critical Care 2025;85:154923 View
  30. Kupferberg A, Hasler G. From antidepressants and psychotherapy to oxytocin, vagus nerve stimulation, ketamine and psychedelics: how established and novel treatments can improve social functioning in major depression. Frontiers in Psychiatry 2024;15 View
  31. Deng T, Urbaczewski A, Lee Y, Barman-Adhikari A, Dewri R. Identifying Marijuana Use Behaviors Among Youth Experiencing Homelessness Using a Machine Learning–Based Framework: Development and Evaluation Study. JMIR AI 2024;3:e53488 View
  32. Dalal S, Jain S, Dave M. Review of Advancements in Depression Detection Using Social Media Data. IEEE Transactions on Computational Social Systems 2025;12(1):77 View
  33. Yan Z, Peng F, Zhang D. DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content. Decision Support Systems 2025;191:114421 View
  34. Rafi M, Islam T, Stanimirović P, Stupina A, Kovalev I, Mourtas S, Sahoo J. Mental health evaluation during internet blackouts: A case study of Bangladesh Quota Movement. ITM Web of Conferences 2025;72:02004 View
  35. Nedungadi P, Veena G, Tang K, Menon R, Raman R. AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling. IEEE Access 2025;13:37389 View
  36. Goh Y, See Q, Vongsirimas N, Klanin‐Yobas P. Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta‐Analysis. Journal of Clinical Nursing 2025 View
  37. Hasan E, Epping G, Lorenzo-Luaces L, Bollen J, Trueblood J, Galea S. One-shot intervention reduces online engagement with distorted content. PNAS Nexus 2025;4(3) View
  38. Phiri D, Makowa F, Amelia V, Phiri Y, Dlamini L, Chung M. Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e59002 View
  39. Zhou S, Mohd M. Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model. IEEE Access 2025;13:63284 View
  40. Islam M, Islam T, Hossain G, Mofazzal Hossain M. Psychological Impact of Internet Blackouts: A Case Study With Machine Learning-Based Stress Analysis. IEEE Access 2025;13:83505 View
  41. Hou K, Hou T, Wang T, Cai L. Exploring the depression sharing on social media platforms: an investigation based on text semantic mining and textual emotion detection. Current Psychology 2025 View

Books/Policy Documents

  1. Jickson S, Anoop V, Asharaf S. Proceedings of International Conference on Information Technology and Applications. View
  2. Kansal M, Singh P, Srivastava P, Singhal R, Deep N, Singh A. Future of AI in Medical Imaging. View
  3. Haldar S, El-Gayar O, El-Gayar S. Impact of Digital Solutions for Improved Healthcare Delivery. View
  4. Alsaedi A, Yafooz W. AI-Driven: Social Media Analytics and Cybersecurity. View
  5. Rohan C, Sapna V. Fifth International Conference on Computing and Network Communications. View
  6. Sambandam R, Vetriveeran D, Jenefa J, Vinodha D, Thaiyalnayaki S. Soft Computing and Signal Processing. View

Conference Proceedings

  1. Zhong M, Van Zoest V, Bilal A, Papadopoulos F, Castellano G. Proceedings of the 2022 International Conference on Multimodal Interaction. Unimodal vs. Multimodal Prediction of Antenatal Depression from Smartphone-based Survey Data in a Longitudinal Study View
  2. Nanavati J, Patel U. 2023 International Conference on Data Science and Network Security (ICDSNS). Hybrid Model for Analysis of Social Media Posts for Identification of Depression and Measuring Its Severity View
  3. Fang C, Dianatobing G, Atara T, Edbert I, Suhartono D. 2022 6th International Conference on Informatics and Computational Sciences (ICICoS). Feature Extraction Methods for Depression Detection Through Social Media Text View
  4. Khaparde A, Das R, Bhargava R. 2023 15th International Conference on Developments in eSystems Engineering (DeSE). Transformer Based Approach for Depression Detection View
  5. Roja S, Durairaj M. FIFTH INTERNATIONAL CONFERENCE ON APPLIED SCIENCES: ICAS2023. Language patterns and sentiment expressions of post-covid patients in social media: A machine learning perspective View
  6. Lorenzoni G, Velmovitsky P, Alencar P, Cowan D. 2024 IEEE International Conference on Big Data (BigData). GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study View
  7. Kavitha G, Veena K. 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). Research on User Behaviour Prediction in Social Networks Based on Deep Learning Model View
  8. Dileep A, Zakkir T, Jayamohan S. THE 6TH INTERNATIONAL CONFERENCE OF ICE-ELINVO 2023: Digital Solutions for Sustainable and Green Development. Predicting depression levels on social media engagement: A machine learning approach View
  9. Markose G. 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT). Leveraging Machine Learning Algorithms for Early Detection of Major Depressive Disorder: A Deep Learning Approach with Twitter Data View
  10. Parasar A, N S, Priya S, S P, Kumar A, Palanimuthu K. 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). Machine Learning-Based Detection of Stress Levels in Students with Social Media Addiction View
  11. Miniyadan A, G P, P N, R R. 2025 Emerging Technologies for Intelligent Systems (ETIS). An Intelligent System for Prediction of Depression based on Facial Emotions and Textual Data View