This paper is in the following e-collection/theme issue:

New Methods (901) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1552) Depression and Mood Disorders; Suicide Prevention (1271) Diagnostic Tools in Mental Health (220)

Published on in Vol 21, No 6 (2019): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12554, first published .
Early Detection of Depression: Social Network Analysis and Random Forest Techniques

Early Detection of Depression: Social Network Analysis and Random Forest Techniques

Early Detection of Depression: Social Network Analysis and Random Forest Techniques

Authors of this article:

Fidel Cacheda1, 2 Author Orcid Image ;   Diego Fernandez1, 2 Author Orcid Image ;   Francisco J Novoa1, 2 Author Orcid Image ;   Victor Carneiro1, 2 Author Orcid Image
Article Authors Cited by (114) Tweetations (99) Metrics

Journals

  1. Majeed A, Rauf I. Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks. Inventions 2020;5(1):10 View
  2. Cho S, Geem Z, Na K. Prediction of suicide among 372,813 individuals under medical check-up. Journal of Psychiatric Research 2020;131:9 View
  3. Hu Y, Chen K, Chang I, Shen C. Critical Predictors for the Early Detection of Conversion From Unipolar Major Depressive Disorder to Bipolar Disorder: Nationwide Population-Based Retrospective Cohort Study. JMIR Medical Informatics 2020;8(4):e14278 View
  4. Moon H, Lee G. Evaluation of Korean-Language COVID-19–Related Medical Information on YouTube: Cross-Sectional Infodemiology Study. Journal of Medical Internet Research 2020;22(8):e20775 View
  5. Wang X, Chen S, Li T, Li W, Zhou Y, Zheng J, Chen Q, Yan J, Tang B. Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis. JMIR Medical Informatics 2020;8(7):e17958 View
  6. Ramírez-Cifuentes D, Freire A, Baeza-Yates R, Puntí J, Medina-Bravo P, Velazquez D, Gonfaus J, Gonzàlez J. Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis. Journal of Medical Internet Research 2020;22(7):e17758 View
  7. Katrakazas C, Antoniou C, Yannis G. Identification of driving simulator sessions of depressed drivers: A comparison between aggregated and time-series classification. Transportation Research Part F: Traffic Psychology and Behaviour 2020;75:16 View
  8. López-Vizcaíno M, Nóvoa F, Carneiro V, Cacheda F. Early detection of cyberbullying on social media networks. Future Generation Computer Systems 2021;118:219 View
  9. Singh A, Singh J. Automation of detection of social network mental disorders – A review. IOP Conference Series: Materials Science and Engineering 2021;1022(1):012008 View
  10. Wong A, Zhou P, Butt Z. Pattern discovery and disentanglement on relational datasets. Scientific Reports 2021;11(1) View
  11. Roy S, Aithal P, Bose R. Judging Mental Health Disorders Using Decision Tree Models. International Journal of Health Sciences and Pharmacy 2021:11 View
  12. Lu Z, Wang J, Li X. Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study. Journal of Medical Internet Research 2021;23(3):e22860 View
  13. Bathina K, ten Thij M, Lorenzo-Luaces L, Rutter L, Bollen J. Individuals with depression express more distorted thinking on social media. Nature Human Behaviour 2021;5(4):458 View
  14. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668 View
  15. Lee B, Lee T, Jeon H, Lee S, Kim K, Cho W, Hwang J, Chae Y, Jung J, Kang H, Kim N, Shin C, Jang J. Synergy through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening: Protocol Design and Feasibility Study (Preprint). JMIR Formative Research 2021 View
  16. Thapa B, Torres I, Koya S, Robbins G, Abdalla S, Arah O, Weeks W, Zhang L, Asma S, Morales J, Galea S, Rhee K, Larson H. Use of Data to Understand the Social Determinants of Depression in Two Middle‐Income Countries: the 3‐D Commission. Journal of Urban Health 2021;98(S1):41 View
  17. Liu J, Shi M. What Are the Characteristics of User Texts and Behaviors in Chinese Depression Posts?. International Journal of Environmental Research and Public Health 2022;19(10):6129 View
  18. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  19. Chatterjee M, Kumar P, Samanta P, Sarkar D. Suicide ideation detection from online social media: A multi-modal feature based technique. International Journal of Information Management Data Insights 2022;2(2):100103 View
  20. Lopez-Vizcaino M, Novoa F, Fernandez D, Cacheda F. Measuring Early Detection of Anomalies. IEEE Access 2022;10:127695 View
  21. Smrke U, Mlakar I, Lin S, Musil B, Plohl N. Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review. JMIR Mental Health 2021;8(12):e30439 View
  22. Wang S, Zhu X, Ding W, Yengejeh A. Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View. IEEE/CAA Journal of Automatica Sinica 2022;9(8):1384 View
  23. Zhou Z, Luo D, Yang B, Liu Z. Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study. Frontiers in Psychiatry 2022;13 View
  24. Babu N, Kanaga E. Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review. SN Computer Science 2022;3(1) View
  25. Vayadande K, Bodhankar A, Mahajan A, Prasad D, Mahajan S, Pujari A, Dhakalkar R, Sengodan T. Classification of Depression on social media using Distant Supervision. ITM Web of Conferences 2022;50:01005 View
  26. Ríssola E, Aliannejadi M, Crestani F. Mental disorders on online social media through the lens of language and behaviour: Analysis and visualisation. Information Processing & Management 2022;59(3):102890 View
  27. Talbot A, Lee C, Ryan S, Roberts N, Mahtani K, Albury C. Experiences of treatment-resistant mental health conditions in primary care: a systematic review and thematic synthesis. BMC Primary Care 2022;23(1) View
  28. Santos W, de Oliveira R, Paraboni I. SetembroBR: a social media corpus for depression and anxiety disorder prediction. Language Resources and Evaluation 2024;58(1):273 View
  29. M. Almars A. Attention-Based Bi-LSTM Model for Arabic Depression Classification. Computers, Materials & Continua 2022;71(2):3091 View
  30. Karakose T, Yıldırım B, Tülübaş T, Kardas A. A comprehensive review on emerging trends in the dynamic evolution of digital addiction and depression. Frontiers in Psychology 2023;14 View
  31. Kmetty Z, Bozsonyi K. Identifying Depression-Related Behavior on Facebook—An Experimental Study. Social Sciences 2022;11(3):135 View
  32. Tshimula J, Chikhaoui B, Wang* S. COVID-19: Detecting depression signals during stay-at-home period. Health Informatics Journal 2022;28(2):146045822210949 View
  33. Schöler D, Kostev K, Demir M, Luedde M, Konrad M, Luedde T, Roderburg C, Loosen S. An Elevated FIB-4 Score Is Associated with an Increased Incidence of Depression among Outpatients in Germany. Journal of Clinical Medicine 2022;11(8):2214 View
  34. Haque U, Kabir E, Khanam R, Wang H. Detection of child depression using machine learning methods. PLOS ONE 2021;16(12):e0261131 View
  35. Nanomi Arachchige I, Sandanapitchai P, Weerasinghe R. Investigating Machine Learning & Natural Language Processing Techniques Applied for Predicting Depression Disorder from Online Support Forums: A Systematic Literature Review. Information 2021;12(11):444 View
  36. Ghosh S, Ekbal A, Bhattacharyya P. What Does Your Bio Say? Inferring Twitter Users’ Depression Status From Multimodal Profile Information Using Deep Learning. IEEE Transactions on Computational Social Systems 2022;9(5):1484 View
  37. 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 2022 View
  38. Tigga N, Garg S. Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals. Health Information Science and Systems 2022;11(1) View
  39. Sarkar D, Kumar P, Samanta P, Dutta S, Chatterjee M. A Two-Level Multi-Modal Analysis for Depression Detection From Online Social Media. International Journal of Software Innovation 2022;10(1):1 View
  40. Smith E, Storch E, Vahia I, Wong S, Lavretsky H, Cummings J, Eyre H. Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in Psychiatry 2021;12 View
  41. Liu J, Shi M. A Hybrid Feature Selection and Ensemble Approach to Identify Depressed Users in Online Social Media. Frontiers in Psychology 2022;12 View
  42. Lee B, Lee T, Jeon H, Lee S, Kim K, Cho W, Hwang J, Chae Y, Jung J, Kang H, Kim N, Shin C, Jang J. Synergy Through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening. IEEE Journal of Biomedical and Health Informatics 2022;26(7):2909 View
  43. Zhou L, Liu Z, Yuan X, Shangguan Z, Li Y, Hu B. CAIINET: Neural network based on contextual attention and information interaction mechanism for depression detection. Digital Signal Processing 2023;137:103986 View
  44. Jiang Z, Lin L, Zhang X, Luan J, Zhao R, Chen L, Lam J, Yip K, So H, Wong W, Ip P, Ngai E. A Data-Driven Context-Aware Health Inference System for Children during School Closures. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(1):1 View
  45. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View
  46. Xue C, Li A, Wu R, Chai J, Qiang Y, Zhao J, Yang Q. VRNPT: A Neuropsychological Test Tool for Diagnosing Mild Cognitive Impairment Using Virtual Reality and EEG Signals. International Journal of Human–Computer Interaction 2023:1 View
  47. Chatterjee M, Kumar P, Sarkar D. Generating a Mental Health Curve for Monitoring Depression in Real Time by Incorporating Multimodal Feature Analysis Through Social Media Interactions. International Journal of Intelligent Information Technologies 2023;19(1):1 View
  48. Han J, Li H, Lin H, Wu P, Wang S, Tu J, Lu J. Depression prediction based on LassoNet-RNN model: A longitudinal study. Heliyon 2023;9(10):e20684 View
  49. García-Noguez L, Tovar-Arriaga S, Paredes-García W, Ramos-Arreguín J, Aceves-Fernandez M. Automatic classification of depressive users on Twitter including temporal analysis. Network Modeling Analysis in Health Informatics and Bioinformatics 2023;12(1) View
  50. Park J, Ahn H, Youn K, Lee M, Hong S. Ensemble Learning to Identify Depression Indicators for Korean Farmers. IEEE Access 2023;11:118787 View
  51. Towler L, Bondaronek P, Papakonstantinou T, Amlôt R, Chadborn T, Ainsworth B, Yardley L. Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques. Frontiers in Public Health 2023;11 View
  52. Jamali A, Berger C, Spiteri R. Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach. JMIR AI 2023;2:e49531 View
  53. Hu Y, Hung J, Hu L, Huang S, Shen C, Ng Q. An analysis of Chinese nursing electronic medical records to predict violence in psychiatric inpatients using text mining and machine learning techniques. PLOS ONE 2023;18(6):e0286347 View
  54. Modi K, Singh I, Kumar Y. A Comprehensive Analysis of Artificial Intelligence Techniques for the Prediction and Prognosis of Lifestyle Diseases. Archives of Computational Methods in Engineering 2023;30(8):4733 View
  55. McIntyre R, Greenleaf W, Bulaj G, Taylor S, Mitsi G, Saliu D, Czysz A, Silvesti G, Garcia M, Jain R. Digital health technologies and major depressive disorder. CNS Spectrums 2023;28(6):662 View
  56. Hasib K, Islam M, Sakib S, Akbar M, Razzak I, Alam M. Depression Detection From Social Networks Data Based on Machine Learning and Deep Learning Techniques: An Interrogative Survey. IEEE Transactions on Computational Social Systems 2023;10(4):1568 View
  57. López-Vizcaíno M, Nóvoa F, Artieres T, Cacheda F. Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks. Sensors 2023;23(10):4788 View
  58. Islam R, Layek M. StackEnsembleMind: Enhancing well-being through accurate identification of human mental states using stack-based ensemble machine learning. Informatics in Medicine Unlocked 2023;43:101405 View
  59. Zhou H, Kulick E. Social Support and Depression among Stroke Patients: A Topical Review. International Journal of Environmental Research and Public Health 2023;20(24):7157 View
  60. Park J, Lee C, Nam Y, Lee H. Association between depressive symptoms and dynamic balance among young adults in the community. Heliyon 2024;10(2):e24093 View
  61. López-Vizcaíno M, Nóvoa F, Fernández D, Cacheda F. Time Aware F-Score for Cybersecurity Early Detection Evaluation. Applied Sciences 2024;14(2):574 View
  62. Królak A, Wiktorski T, Żmudzińska A. Automatic analysis of X (Twitter) data for supporting depression diagnosis. Human Technology 2023;19(3):370 View
  63. Dou R, Kang X. TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis. Computers and Electrical Engineering 2024;114:109071 View
  64. Ghafori S, Yousefi Z, Bakhtiari E, mohammadi mahdiabadi hasani m, Hassanzadeh G. Neutrophil-to-lymphocyte ratio as a predictive biomarker for early diagnosis of depression: A narrative review. Brain, Behavior, & Immunity - Health 2024;36:100734 View
  65. Xu X, An F, Wu S, Wang H, Kang Q, Wang Y, Zhu T, Zhang B, Huang W, Liu X, Wang X. Affective norms for 501 Chinese words from three emotional dimensions rated by depressive disorder patients. Frontiers in Psychiatry 2024;15 View
  66. Pourkeyvan A, Safa R, Sorourkhah A. Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks. IEEE Access 2024;12:28025 View
  67. K C, Reddy G, Gari Anil Kumar Reddy M, Rohith Raj K, Harsha S, Kiran R, Singla T, Satyanarayana K, Bobba P, Perveen A, Debnath S. A Novel Approach for Analysis and Detection of Depression Using Electroencephalogram (EEG) Signals. MATEC Web of Conferences 2024;392:01101 View
  68. 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
  69. Liu Q, Su F, Mu A, Wu X. Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory. Psychology Research and Behavior Management 2024;Volume 17:1587 View
  70. de Oliveira R, Trevisan Martins J, Paraboni I. Mental health prediction from social media connections. New Review of Hypermedia and Multimedia 2023;29(3-4):225 View
  71. Liu J, Gao M, Zhang R, Wong N, Wu J, Chan C, Lee T. A machine-learning approach to model risk and protective factors of vulnerability to depression. Journal of Psychiatric Research 2024;175:374 View
  72. Yang M, Li Z, Gao Y, He C, Huang F, Chen W. Heterogeneous Graph Attention Networks for Depression Identification by Campus Cyber-Activity Patterns. IEEE Transactions on Computational Social Systems 2024;11(3):3493 View

Books/Policy Documents

  1. Hemtanon S, Aekwarangkoon S, Kittiphattanabawon N. Recent Advances in Information and Communication Technology 2021. View
  2. Bhayani A, Meshram P, Desai B, Garg A, Jha S. Communication and Intelligent Systems. View
  3. Agarwal S, M K, Singh P, Shah J, Sanjeev N. Neural Information Processing. View
  4. Samanta P, Kumar P, Dutta S, Chatterjee M, Sarkar D. Data Management, Analytics and Innovation. View
  5. Rawat T, Jain S. Artificial Intelligence, Machine Learning, and Mental Health in Pandemics. View
  6. Nor N, Rahman N, Yaakub M, Zukarnain Z. Intelligent Computing. View
  7. Kumar A, Pratihar V, Kumar S, Abhishek K. Machine Vision and Augmented Intelligence—Theory and Applications. View
  8. Biilah M, Raihan M, Akter T, Alvi N, Bristy N, Rehana H. International Conference on Innovative Computing and Communications. View
  9. Yohapriyaa M, Uma M. Intelligent Data Communication Technologies and Internet of Things. View
  10. Smith E, Storch E, Lavretsky H, Cummings J, Eyre H. Handbook of Computational Neurodegeneration. View
  11. Bollen J, ten Thij M, Lorenzo-Luaces L, Rutter L. Early Detection of Mental Health Disorders by Social Media Monitoring. View
  12. Chen X, Genc Y. Artificial Intelligence in HCI. View
  13. Lia R, Siddikk A, Muntasir F, Rahman S, Jahan N. Big Data Intelligence for Smart Applications. View
  14. Das B, Das B, Chatterjee A, Das A. Cyber-Physical Systems. View
  15. Kaywan P, Ahmed K, Miao Y, Ibaida A, Gu B. Health Information Science. View
  16. Pérez A, Piot-Pérez-Abadín P, Parapar J, Barreiro Á. Advances in Information Retrieval. View
  17. Chatterjee M, Modak S, Sarkar D. Cognitive Cardiac Rehabilitation Using IoT and AI Tools. View
  18. Haque U, Kabir E, Khanam R. Health Information Science. View
  19. Smith E, Storch E, Lavretsky H, Cummings J, Eyre H. Handbook of Computational Neurodegeneration. View
  20. Abuhasirah Y. Artificial Intelligence-Augmented Digital Twins. View
  21. Nag A, Bandyopadhyay A, Nayak T, Banerjee S, Panda B, Mishra S. Machine Intelligence for Research and Innovations. View
  22. Gorrab A, Ben Rabah N, Le Grand B, Deneckère R, Bonnerot T. Advanced Information Networking and Applications. View