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

Theme Issue 2021: Health Natural Language Processing and Applications (16) Depression and Mood Disorders; Suicide Prevention (1363) Natural Language Processing (692) Machine Learning (1465)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28754, first published .
Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

Depression Detection on Reddit With an Emotion-Based Attention Network: Algorithm Development and Validation

Authors of this article:

Lu Ren1 Author Orcid Image ;   Hongfei Lin1 Author Orcid Image ;   Bo Xu1, 2 Author Orcid Image ;   Shaowu Zhang1 Author Orcid Image ;   Liang Yang1 Author Orcid Image ;   Shichang Sun3 Author Orcid Image
Article Authors Cited by (41) Tweetations (3) Metrics

Journals

  1. Zhang W, Sun T, Wang S, Zhang J, Yang M, Li Z. Influence of preoperative depression on clinical outcomes after cervical laminoplasty: A retrospective study. Frontiers in Surgery 2023;9 View
  2. Li Z, An Z, Cheng W, Zhou J, Zheng F, Hu B. MHA: a multimodal hierarchical attention model for depression detection in social media. Health Information Science and Systems 2023;11(1) View
  3. 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
  4. Zhang T, Yang K, Ji S, Ananiadou S. Emotion fusion for mental illness detection from social media: A survey. Information Fusion 2023;92:231 View
  5. Yang K, Zhang T, Ananiadou S. A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media. Information Processing & Management 2022;59(4):102961 View
  6. Adarsh V, Arun Kumar P, Lavanya V, Gangadharan G. Fair and Explainable Depression Detection in Social Media. Information Processing & Management 2023;60(1):103168 View
  7. Wang H, Liu Y, Zhen X, Tu X. Depression Speech Recognition With a Three-Dimensional Convolutional Network. Frontiers in Human Neuroscience 2021;15 View
  8. Cui B, Wang J, Lin H, Zhang Y, Yang L, Xu B. Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation. JMIR Medical Informatics 2022;10(8):e37818 View
  9. Owen D, Antypas D, Hassoulas A, Pardiñas A, Espinosa-Anke L, Collados J. Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation. JMIR AI 2023;2:e41205 View
  10. Liang Y, Liu L, Ji Y, Huangfu L, Zeng D. Identifying emotional causes of mental disorders from social media for effective intervention. Information Processing & Management 2023;60(4):103407 View
  11. Saha T, Reddy S, Saha S, Bhattacharyya P. Mental Health Disorder Identification From Motivational Conversations. IEEE Transactions on Computational Social Systems 2023;10(3):1130 View
  12. Dalal S, Jain S, Dave M. An Investigation of Data Requirements for the Detection of Depression from Social Media Posts. Recent Patents on Engineering 2022;17(3) View
  13. Ilias L, Askounis D. Multitask learning for recognizing stress and depression in social media. Online Social Networks and Media 2023;37-38:100270 View
  14. 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
  15. Dalal S, Jain S, Dave M. Convolution Neural Network Having Multiple Channels with Own Attention Layer for Depression Detection from Social Data. New Generation Computing 2024;42(1):135 View
  16. Malhotra A, Jindal R. XAI Transformer based Approach for Interpreting Depressed and Suicidal User Behavior on Online Social Networks. Cognitive Systems Research 2024;84:101186 View
  17. 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
  18. Liu J, Chen W, Wang L, Ding F. A hybrid depression detection model and correlation analysis for social media based on attention mechanism. International Journal of Machine Learning and Cybernetics 2024;15(7):2631 View
  19. Mieles Toloza I, Delgado Meza J, Acevedo-Suárez J. Análisis del Lenguaje Natural para la Identificación de Alteraciones Mentales en Redes Sociales: Una Revisión Sistemática de Estudios. Revista Politécnica 2024;53(1):57 View
  20. TENG S, LIU J, HUANG Y, CHAI S, TATEYAMA T, HUANG X, LIN L, CHEN Y. An Intra- and Inter-Emotion Transformer-Based Fusion Model with Homogeneous and Diverse Constraints Using Multi-Emotional Audiovisual Features for Depression Detection. IEICE Transactions on Information and Systems 2024;E107.D(3):342 View
  21. Cabral R, Han S, Poon J, Nenadic G. MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media. Robotics 2024;13(3):53 View
  22. 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
  23. Ilias L, Mouzakitis S, Askounis D. Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media. IEEE Transactions on Computational Social Systems 2024;11(2):1979 View
  24. Zhu J, Jin R, Kenne D, Phan N, Ku W. User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights From Overlapping r/SuicideWatch Users. Journal of Medical Internet Research 2024;26:e53968 View
  25. Chen J, Liu S, Xu M, Wang P. Enhancing depression detection: A multimodal approach with text extension and content fusion. Expert Systems 2024;41(10) View
  26. Montejo-Ráez A, Molina-González M, Jiménez-Zafra S, García-Cumbreras M, García-López L. A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges. Computer Science Review 2024;53:100654 View
  27. van Buchem M, de Hond A, Fanconi C, Shah V, Schuessler M, Kant I, Steyerberg E, Hernandez-Boussard T. Applying natural language processing to patient messages to identify depression concerns in cancer patients. Journal of the American Medical Informatics Association 2024;31(10):2255 View
  28. Sumedrea A, Sumedrea C, Săvulescu F. A Computing System for Complex Cases of Major Recurrent Depression Based on Latent Semantic Analysis: Relationship between Life Themes and Symptoms. Big Data and Cognitive Computing 2024;8(8):88 View
  29. Liu Z, Wu Y, Zhang H, Li G, Ding Z, Hu B. Stimulus-Response Patterns: The Key to Giving Generalizability to Text-Based Depression Detection Models. IEEE Journal of Biomedical and Health Informatics 2024;28(8):4925 View
  30. Qin R, Cook R, Yang K, Abbasi A, Dobolyi D, Seyedi S, Griner E, Kwon H, Cotes R, Jiang Z, Clifford G. Language Models for Online Depression Detection: A Review and Benchmark Analysis on Remote Interviews. ACM Transactions on Management Information Systems 2024 View
  31. Rahman A, Ta H, Najjar L, Azadmanesh A, Gönul A. DepressionEmo: A novel dataset for multilabel classification of depression emotions. Journal of Affective Disorders 2024;366:445 View
  32. Abdullah M, Negied N. Detection and Prediction of Future Mental Disorder From Social Media Data Using Machine Learning, Ensemble Learning, and Large Language Models. IEEE Access 2024;12:120553 View

Books/Policy Documents

  1. Dalal S, Jain S, Dave M. Proceedings of the International Health Informatics Conference. View
  2. Jickson S, Anoop V, Asharaf S. Proceedings of International Conference on Information Technology and Applications. View
  3. Zhu W, Zhang Y, Yu X, Lu M, Lin H. Health Information Processing. View