Early Detection of Depression: Social Network Analysis and Random Forest Techniques
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Abstract
BACKGROUND: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. OBJECTIVE: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects' behavior based on different aspects of their writings: textual spreading, time gap, and time span. METHODS: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. RESULTS: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. CONCLUSIONS: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.
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The global prevalence of depression has steadily increased from 1990 to 2019.
“The global prevalence of depression has steadily increased from 1990 to 2019, 1”
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population
Radloff, L. S. (1977) · Applied Psychological Measurement · doi:10.1177/014662167700100306 · Cited by 53,267 · see citing works
The application of electronic computers to factor analysis
Kaiser, H. F. (1960) · Educational and Psychological Measurement · doi:10.1177/001316446002000116 · Cited by 12,459 · see citing works
Zhang, Q., Wang, C., Zheng, M., Li, Y., Li, J., Zhang, L., Shang, X., & Yan, C. (2014) · Cerebrovascular Diseases · doi:10.1159/000369778 · Cited by 11,609 · see citing works
Projections of Global Mortality and Burden of Disease from 2002 to 2030
Mathers, C. D., & Loncar, D. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030442 · Cited by 11,386 · see citing works
An introduction to latent semantic analysis
Landauer, T. K., Foltz, P. W., & Laham, D. (1998) · Discourse Processes · doi:10.1080/01638539809545028 · Cited by 4,880 · see citing works
The probabilistic relevance framework: BM25 and beyond
Robertson, S., & Zaragoza, H. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000019 · Cited by 3,374 · see citing works
Psychological Aspects of Natural Language Use: Our Words, Our Selves
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624 · see citing works
The global burden of mental disorders: An update from the WHO World Mental Health (WMH) surveys
Kessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., Üstün, T. B., & Wang, P. S. (2009) · Epidemiologia e Psichiatria Sociale · doi:10.1017/s1121189x00001421 · Cited by 1,895 · see citing works
Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak
Chew, C., & Eysenbach, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014118 · Cited by 1,614 · see citing works
In Defense of One-Vs-All Classification
Rifkin, R. M., & Junior, A. B. D. R. K. (2004) · Cited by 1,598 · see citing works
Readings in information retrieval
Jones, K. S., & Willett, P. (1997) · Morgan Kaufmann Publishers Inc. eBooks · Cited by 1,062 · see citing works
Smarr, K. L., & Keefer, A. L. (2011) · Arthritis Care & Research · doi:10.1002/acr.20556 · Cited by 971 · see citing works
Detecting depression and mental illness on social media: an integrative review
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.005 · Cited by 722 · see citing works
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Choudhury, M. D., Kıcıman, E., Dredze, M. H., Coppersmith, G., & Kumar, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 682 · see citing works
Hamilton, M. (1980) · PubMed · Cited by 471 · see citing works
Feature normalization and likelihood-based similarity measures for image retrieval
Aksoy, S., & Haralick, R. M. (2001) · Pattern Recognition Letters · doi:10.1016/s0167-8655(00)00112-4 · Cited by 465 · see citing works
Chunara, R., Andrews, J. R., & Brownstein, J. S. (2012) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.2012.11-0597 · Cited by 437 · see citing works
CLPsych 2015 Shared Task: Depression and PTSD on Twitter
Coppersmith, G., Dredze, M. H., Harman, C., Hollingshead, K., & Mitchell, M. (2015) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.3115/v1/w15-1204 · Cited by 329 · see citing works
Institute of Medicine report on prevention of mental disorders: Summary and commentary.
Muñoz, R. F., Mrazek, P. J., & Haggerty, R. J. (1996) · American Psychologist · doi:10.1037//0003-066x.51.11.1116 · Cited by 305 · see citing works
Researching Mental Health Disorders in the Era of Social Media: Systematic Review
Wongkoblap, A., Vadillo, M. A., & Ćurčin, V. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7215 · Cited by 269 · see citing works
A Test Collection for Research on Depression and Language Use
Losada, D. E., & Crestani, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44564-9_3 · Cited by 244 · see citing works
Social media, big data, and mental health: current advances and ethical implications
Conway, M., & O’Connor, D. (2016) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2016.01.004 · Cited by 231 · see citing works
Institute of Medicine report on prevention of mental disorders: Summary and commentary.
Muñoz, R. F., Mrazek, P. J., & Haggerty, R. J. (1996) · American Psychologist · doi:10.1037/0003-066x.51.11.1116 · Cited by 222 · see citing works
Rost, K., Smith, J. L., & Dickinson, M. (2004) · Medical Care · doi:10.1097/00005650-200412000-00007 · Cited by 215 · see citing works
Rice, S. M., Goodall, J., Hetrick, S. E., Parker, A. G., Gilbertson, T., Amminger, G. P., Davey, C. G., McGorry, P. D., Gleeson, J., & Alvarez-Jimenez, M. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3304 · Cited by 194 · see citing works
Flaxman, A. D., Vahdatpour, A., Green, S. T., James, S. L., & Murray, C. J. L. (2011) · Population Health Metrics · doi:10.1186/1478-7954-9-29 · Cited by 187 · see citing works
Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7956 · Cited by 168 · cited 1× · see citing works
“…to derive distinct markers of shifts to suicidal ideation from Reddit, Inc user data for modeling in a prediction framework, and Birnbaum et al [25] proposed a method that used machine learning in combination with clinical appraisals as a means of identifying social media markers of…”
eRISK 2017: CLEF Lab on Early Risk Prediction on the Internet: Experimental Foundations
Losada, D. E., Crestani, F., & Parapar, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-65813-1_30 · Cited by 164 · see citing works
Detecting and Characterizing Mental Health Related Self-Disclosure in Social Media
Balani, S., & Choudhury, M. D. (2015) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2702613.2732733 · Cited by 163 · see citing works
Depression: the benefits of early and appropriate treatment.
Halfin, A. (2007) · PubMed · Cited by 160 · see citing works
Twitter: A Good Place to Detect Health Conditions
Prieto, V. M., Matos, S., Álvarez, M., Cacheda, F., & Oliveira, J. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086191 · Cited by 157 · see citing works
Self-citationDetecting Suicidal Ideation on Forums: Proof-of-Concept Study
Aladağ, A. E., Müderrisoğlu, S., Akbaş, N. B., Zahmacıoğlu, O., & Bingol, H. O. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9840 · Cited by 148 · see citing works
Talking about your health to strangers: Understanding the use of online social networks by patients
Colineau, N., & Paris, C. (2010) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2010.496131 · Cited by 126 · see citing works
Cameron, I. M., Cardy, A., Crawford, J. R., du Toit, S. W., Hay, S., Lawton, K., Mitchell, K., Sharma, S., Shivaprasad, S., Winning, S., et al. (2011) · British Journal of General Practice · doi:10.3399/bjgp11x583209 · Cited by 109 · see citing works
Screening Twitter Users for Depression and PTSD with Lexical Decision Lists
Pedersen, T. (2015) · doi:10.3115/v1/w15-1206 · Cited by 61 · see citing works
Picardi, A., Lega, I., Tarsitani, L., Caredda, M., Matteucci, G., Zerella, M. P., Miglio, R., Gigantesco, A., Cerbo, M., Gaddini, A., et al. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.03.025 · Cited by 55 · see citing works
Ziemer, K. S., & Korkmaz, G. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.06.038 · Cited by 47 · see citing works
The importance of reviewing the code
Añel, J. A. (2011) · Communications of the ACM · doi:10.1145/1941487.1941502 · Cited by 36 · see citing works
Unfolding the notes from the walls: Adolescents’ depression manifestations on Facebook
Ophir, Y., Asterhan, C. S. C., & Schwarz, B. B. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.02.013 · Cited by 24 · see citing works
Flagged first, then by citations
Research works printed but not linked: 9
- [14]Park M McDonald D Cha M Perception differences between the depressed and non-depressed users in Twitter 2013 07 International AAAI Conference on Web and Social Media (ICWSM). The AAAI Press July, 2013 Cambridge, Massachusetts, USA
- [15]De Choudhury M Gamon M Counts S Horvitz E Predicting depression via social media 2013 07 Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media; July 2013 Cambridge, Massachusetts, USA 128 137
- [30]Nadeem M arXiv 2016 2019-06-04 Identifying depression on Twitter https://arxiv.org/ftp/arxiv/papers/1607/1607.07384.pdf
- [33]Resnik P Armstrong W Claudino L Nguyen T The University of Maryland CLPsych 2015 shared task system 2015 Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality June 5, 2015 Denver, Colorado, USA 54 60 10.3115/v1/W15-1204 · DOI
- [38]Trotzek M Koitka S Friedrich C Linguistic metadata augmented classifiers at the CLEF 2017 task for early detection of depression 2017 Conference Labs of the Evaluation Forum September 11-14, 2017 Dublin, Ireland
- [39]Villegas M Funez D Ucelay M Cagnina L Errecalde M LIDIC - UNSL's Participation at eRisk 2017: pilot task on early detection of depression 2017 Conference Labs of the Evaluation Forum September 11-14, 2017 Dublin, Ireland
- [40]Singhal A IEEE Computer Society Technical Committee on Data Engineering 2001 2019-06-04 Modern information retrieval: a brief overview http://singhal.info/ieee2001.pdf
- [44]Wild F Stahl C Stermsek G Neumann G Parameters Driving Effectiveness of Automated Essay Scoring with LSA 2005 Proceedings of the 9th International Computer Assisted Assessment Conference (CAA) July, 2005 Loughborough, UK 485 494
- [47]Hall M Frank E Holmes G Pfahringer B Reutemann P Witten IH SIGKDD Explorations 2009 11 16 2019-06-04 The WEKA data mining software: an update https://www.kdd.org/exploration_files/p2V11n1.pdf
Cited by
Sorted by citations
Babu, N. V., & Kanaga, E. G. M. (2021) · SN Computer Science · doi:10.1007/s42979-021-00958-1 · Cited by 329 · see citing works
Algorithmic realism: expanding the boundaries of algorithmic thought
Green, B., & Viljoen, S. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372840 · Cited by 161 · see citing works
Majeed, A., & Rauf, I. (2020) · Inventions · doi:10.3390/inventions5010010 · Cited by 153 · see citing works
Hasib, K. M., Islam, M. R., Sakib, S., Akbar, M. A., Razzak, I., & Alam, M. S. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3263128 · Cited by 147 · see citing works
Moon, H., & Lee, G. H. (2020) · Journal of Medical Internet Research · doi:10.2196/20775 · Cited by 139 · see citing works
Baclic, O., Tunis, M. C., Young, K., Doan, C., & Swerdfeger, H. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a02 · Cited by 136 · see citing works
Individuals with depression express more distorted thinking on social media
Bathina, K. C., ten Thij, M., Lorenzo‐Luaces, L., Rutter, L. A., & Bollen, J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01050-7 · Cited by 137 · see citing works
Detection of child depression using machine learning methods
Haque, U. M., Kabir, E., & Khanam, R. (2021) · PLoS ONE · doi:10.1371/journal.pone.0261131 · Cited by 117 · see citing works
Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis
Ramírez‐Cifuentes, D., Freire, A., Baeza-Yates, R. A., Puntí, J., Bravo, P. M., Velazquez, D. A., Gonfaus, J. M., & Gonzàlez, J. (2020) · Journal of Medical Internet Research · doi:10.2196/17758 · Cited by 115 · see citing works
Gooding, P., & Kariotis, T. (2021) · JMIR Mental Health · doi:10.2196/24668 · Cited by 100 · see citing works
Early detection of cyberbullying on social media networks
López-Vizcaíno, M. F., Novoa, F. J., Carneiro, V., & Cacheda, F. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.01.006 · Cited by 100 · see citing works
Self-citationSadeghi, M., Richer, R., Egger, B., Schindler‐Gmelch, L., Rupp, L. H., Rahimi, F., Berking, M., & Eskofier, B. M. (2024) · npj Mental Health Research · doi:10.1038/s44184-024-00112-8 · Cited by 95 · see citing works
Depression detection in social media posts using transformer-based models and auxiliary features
Kerasiotis, M., Ilias, L., & Askounis, D. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01360-4 · Cited by 78 · see citing works
Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis
Wang, X., Chen, S., Li, T., Li, W., Zhou, Y., Zheng, J. H., Chen, Q., Yan, J., & Tang, B. (2020) · JMIR Medical Informatics · doi:10.2196/17958 · Cited by 71 · see citing works
Pourkeyvan, A., Safa, R., & Sorourkhah, A. (2024) · IEEE Access · doi:10.1109/access.2024.3366653 · Cited by 69 · see citing works
Cost-Sensitive Boosting Pruning Trees for Depression Detection on Twitter
Tong, L., Liu, Z., Jiang, Z., Zhou, F., Chen, L., Lyu, J., Zhang, X., Zhang, Q., Sadka, A. H., Wang, Y., et al. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3145634 · Cited by 64 · see citing works
Karaköse, T., Yıldırım, B., Tülübaş, T., & Kardas, A. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1126815 · Cited by 62 · see citing works
Depression detection in social media comments data using machine learning algorithms
Vasha, Z. N., Sharma, B., Esha, I. J. K., Nahian, J. A., & Polin, J. A. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v12i2.4182 · Cited by 55 · see citing works
Attention-Based Bi-LSTM Model for Arabic Depression Classification
Almars, A. M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.022609 · Cited by 55 · see citing works
Ghosh, S. K., Ekbal, A., & Bhattacharyya, P. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3116242 · Cited by 54 · see citing works
Suicide ideation detection from online social media: A multi-modal feature based technique
Chatterjee, M., Kumar, P., Samanta, P., & Sarkar, D. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100103 · Cited by 53 · see citing works
Barua, P. D., Vicnesh, J., Lih, O. S., Palmer, E. E., Yamakawa, T., Kobayashi, M., & Acharya, U. R. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09904-0 · Cited by 53 · see citing works
Explainable depression symptom detection in social media
Bao, E., Pérez, A., & Parapar, J. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00303-9 · Cited by 46 · see citing works
Towler, L. B., Bondaronek, P., Papakonstantinou, T., Amlôt, R., Chadborn, T., Ainsworth, B., & Yardley, L. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1268223 · Cited by 46 · see citing works
Arif, M. (2021) · Journal of Information Security and Cybercrimes Research · doi:10.26735/gbtv9013 · Cited by 45 · see citing works
Zhang, L., Qin, P., Ying, H., Du, Z., Lu, C., Chen, M., Lei, L., Song, Z., Chen, J., Yuan, X., et al. (2025) · Exploration · doi:10.1002/exp.20240232 · Cited by 44 · see citing works
Ríssola, E. A., Aliannejadi, M., & Crestani, F. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102890 · Cited by 44 · see citing works
Arachchige, I. A. N., Sandanapitchai, P., & Weerasinghe, R. (2021) · Information · doi:10.3390/info12110444 · Cited by 41 · see citing works
A Hybrid Feature Selection and Ensemble Approach to Identify Depressed Users in Online Social Media
Liu, J., & Shi, M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.802821 · Cited by 41 · see citing works
Hanif, A., Herman, H., Mudinillah, A., & Rahmi, P. W. L. (2023) · International Journal of Membrane Science and Technology · doi:10.15379/ijmst.v10i2.1207 · Cited by 40 · see citing works
Xin, C., & Zakaria, L. Q. (2024) · IEEE Access · doi:10.1109/access.2024.3488081 · Cited by 40 · see citing works
Lee, B., Lee, T., Jeon, H., Lee, S., Kim, K., Cho, W., Hwang, J., Chae, Y., Jung, J., Kang, H. J., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3147847 · Cited by 39 · see citing works
Digital health technologies and major depressive disorder
McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
Tigga, N. P., & Garg, S. (2022) · Health Information Science and Systems · doi:10.1007/s13755-022-00205-8 · Cited by 37 · see citing works
Machine Learning on Early Diagnosis of Depression
Lee, K.-S., & Ham, B. (2022) · Psychiatry Investigation · doi:10.30773/pi.2022.0075 · Cited by 35 · see citing works
Kerth, J.-L., Hagemeister, M., Bischops, A., Reinhart, L., Dukart, J., Heinrichs, B., Eickhoff, S. B., & Meißner, T. (2024) · European Journal of Pediatrics · doi:10.1007/s00431-024-05846-3 · Cited by 34 · see citing works
Modi, K., Singh, I., & Kumar, Y. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09957-2 · Cited by 34 · see citing works
Social Support and Depression among Stroke Patients: A Topical Review
Zhou, H., & Kulick, E. R. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20247157 · Cited by 32 · see citing works
Affective Computing for Late-Life Mood and Cognitive Disorders
Smith, E., Storch, E. A., Vahia, I. V., Wong, S. T. C., Lavretsky, H., Cummings, J. L., & Eyre, H. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.782183 · Cited by 31 · see citing works
SetembroBR: a social media corpus for depression and anxiety disorder prediction
dos Santos, W. R., de Oliveira, R. L., & Paraboni, I. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-022-09633-0 · Cited by 31 · see citing works
Smrke, U., Mlakar, I., Lin, S. M., Musil, B., & Plohl, N. (2021) · JMIR Mental Health · doi:10.2196/30439 · Cited by 30 · see citing works
Early Detection of Depression from Social Media Data Using Machine Learning Algorithms
Geetha, G. M. N., Saranya, G., Chakrapani, K., Ponsam, J. G., Safa, M., & Karpagaselvi, S. (2020) · International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) · doi:10.1109/icpects49113.2020.9336974 · Cited by 29 · see citing works
Detecting signs of depression on social media: A machine learning analysis and evaluation
Ta, P., Tran, N., Nguyen, H. T., & Nguyen, H. D. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100827 · Cited by 27 · see citing works
Chahar, R., Dubey, A. K., & Narang, S. K. (2021) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2021.874198 · Cited by 26 · see citing works
Zhou, L., Liu, Z., Yuan, X., Shangguan, Z., Li, Y., & Hu, B. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.103986 · Cited by 26 · see citing works
A Systematic Review of Machine Learning Approaches for Mental Disorder Prediction on Social Media
Jiang, Q. (2020) · doi:10.1109/cds49703.2020.00091 · Cited by 25 · see citing works
Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View
Wang, S., Xingquan Zhu, A., Ding, W., & Yengejeh, A. A. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105740 · Cited by 25 · see citing works
TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis
Dou, R., & Kang, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.109071 · Cited by 25 · see citing works
Zhou, Z., Luo, D., Yang, B. X., & Liu, Z. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.876995 · Cited by 23 · see citing works
Ghafori, S. S., Yousefi, Z., Bakhtiari, E., hasani, M. H. M. M., & Hassanzadeh, G. (2024) · Brain Behavior & Immunity - Health · doi:10.1016/j.bbih.2024.100734 · Cited by 23 · see citing works
Naulegari, J., & Kumaresh, N. (2022) · Traitement du signal · doi:10.18280/ts.390109 · Cited by 22 · see citing works
Depression Detection from Social Media Using Twitter’s Tweet
Lia, R. J., Siddikk, A. B., Muntasir, F., Rahman, S. S. M. M., & Jahan, N. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-87954-9_9 · Cited by 18 · see citing works
Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach
Jamali, A. A., Berger, C., & Spiteri, R. J. (2023) · JMIR AI · doi:10.2196/49531 · Cited by 16 · see citing works
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Xu, X., Fu, C., Camacho, D., Park, J. H., & Chen, J. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10327-8 · Cited by 16 · see citing works
Hu, Y., Chen, K., Chang, I., & Shen, C. (2020) · JMIR Medical Informatics · doi:10.2196/14278 · Cited by 16 · see citing works
Hu, Y., Hung, J., Hu, L., Huang, S., & Shen, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286347 · Cited by 15 · see citing works
Zhang, T., Yang, K., & Ananiadou, S. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.9 · Cited by 15 · see citing works
Islam, R., & Layek, M. A. (2023) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2023.101405 · Cited by 15 · see citing works
Khan, A., & Ali, R. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01205-0 · Cited by 15 · see citing works
Prediction of suicide among 372,813 individuals under medical check-up
Cho, S., Geem, Z. W., & Na, K. (2020) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2020.08.035 · Cited by 15 · see citing works
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Han, J., Li, H., Lin, H., Wu, P., Wang, S., Tu, J., & Lu, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20684 · Cited by 15 · see citing works
Fernandes, G. J., Choi, A., Schauer, J. M., Pfammatter, A. F., Spring, B. J., Darwiche, A. Y., & Alshurafa, N. I. (2023) · Journal of Medical Internet Research · doi:10.2196/42047 · Cited by 14 · see citing works
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Xue, C., Li, A., Wu, R., Chai, J., Qiang, Y., Zhao, J., & Yang, Q. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2250605 · Cited by 13 · see citing works
Liu, Q., Su, F., Mu, A., & Wu, X. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s450934 · Cited by 13 · see citing works
Beniwal, R., & Saraswat, P. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109786 · Cited by 13 · see citing works
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Pérez, A., Warikoo, N., Wang, K., Parapar, J., & Gurevych, I. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1000 · Cited by 12 · see citing works
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Wong, A. K. C., Zhou, P.-Y., & Butt, Z. A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84869-4 · Cited by 12 · see citing works
Thapa, B. B., Torres, I., Koya, S. F., Robbins, G., Abdalla, S. M., Arah, O. A., Weeks, W. B., Zhang, L., Asma, S., Morales, J. V., et al. (2021) · Journal of Urban Health · doi:10.1007/s11524-021-00559-6 · Cited by 12 · see citing works
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Yang, M., Li, Z., Gao, Y., He, C., Huang, F., & Chen, W. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3343689 · Cited by 12 · see citing works
Chookerd, N., Mettarikanon, D., Tawanwongsri, W., Puangsri, P., Kaeophanuek, S., Boonpit, V., & Wanchai, A. (2025) · Emerging Science Journal · doi:10.28991/esj-2024-sied1-015 · Cited by 11 · see citing works
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Rahimapandi, H. D. A., Maskat, R., Musa, R., & Ardi, N. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i3.pp1108-1118 · Cited by 12 · see citing works
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Sajja, G. S. (2021) · International Journal of Computer Applications · doi:10.5120/ijca2021921856 · Cited by 11 · see citing works
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Kmetty, Z., & Bozsonyi, K. (2022) · Social Sciences · doi:10.3390/socsci11030135 · Cited by 10 · see citing works
Talbot, A., Lee, C., Ryan, S., Roberts, N., Mahtani, K. R., & Albury, C. (2022) · BMC Family Practice · doi:10.1186/s12875-022-01819-3 · Cited by 10 · see citing works
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Tebar, B., & Gopalan, A. (2021) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase52844.2021.00042 · Cited by 10 · see citing works
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Chanda, K., Bhattacharjee, P., Roy, S., & Biswas, S. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9197843 · Cited by 10 · see citing works
Burbano, R. N., Rendón, O. M. C., & Astudillo, C. A. (2025) · Applied Sciences · doi:10.3390/app15063358 · Cited by 9 · see citing works
Katrakazas, C., Antoniou, C., & Yannis, G. (2020) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2020.09.015 · Cited by 9 · see citing works
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Liu, J., & Shi, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19106129 · Cited by 9 · see citing works
Ariani, T. A., Anna, A., Rahayu, H. T., Aini, N., Windarwati, H. D., Hernawaty, T., Mudiyanselage, S. P. K., & Lin, M. (2023) · Jurnal NERS · doi:10.20473/jn.v18i3.47313 · Cited by 8 · see citing works
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Nag, A., Bandyopadhyay, A., Nayak, T., Banerjee, S., Panda, B., & Mishra, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8129-8_8 · Cited by 8 · see citing works
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Liu, J. M., Gao, M., Zhang, R., Wong, N. M. L., Wu, J., Chan, C. C. H., & Lee, T. M. C. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.04.048 · Cited by 8 · see citing works
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López-Vizcaíno, M. F., Novoa, F. J., Fernández, D., & Cacheda, F. (2024) · Applied Sciences · doi:10.3390/app14020574 · Cited by 8 · see citing works
Self-citationPredictive Modelling of Mental Health Disorders Using Machine Learning Classifiers
Sharma, N., Patel, I. S., Singh, N. K., & Mishra, B. K. (2024) · International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · doi:10.1109/icaiqsa64000.2024.10882408 · Cited by 8 · see citing works
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Nandanwar, H., & Nallamolu, S. (2021) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat52182.2021.9587695 · Cited by 8 · see citing works
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Rawat, T., & Jain, S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91196-2.00002-8 · Cited by 8 · see citing works
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Kaur, V., & Gupta, K. (2023) · doi:10.1109/icaia57370.2023.10169520 · Cited by 8 · see citing works
Chatterjee, M., Kumar, P., & Sarkar, D. (2023) · International Journal of Intelligent Information Technologies · doi:10.4018/ijiit.324600 · Cited by 7 · see citing works
A Data Mining Approach to Identify the Stress Level Based on Different Activities of Human
Billah, M. A.-M., Raihan, M., Alvi, N., Akter, T., & Bristy, N. J. (2021) · International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) · doi:10.1109/icict4sd50815.2021.9396896 · Cited by 7 · see citing works
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López-Vizcaíno, M. F., Novoa, F. J., Fernández, D., & Cacheda, F. (2022) · IEEE Access · doi:10.1109/access.2022.3224467 · Cited by 7 · see citing works
Self-citationSchöler, D., Kostev, K., Demir, M., Luedde, M., Konrad, M., Luedde, T., Roderburg, C., & Loosen, S. H. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082214 · Cited by 7 · see citing works
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Sarkar, D., Kumar, P., Samanta, P., Dutta, S., & Chatterjee, M. (2022) · International Journal of Software Innovation · doi:10.4018/ijsi.309114 · Cited by 7 · see citing works
Classification of Depression on social media using Distant Supervision
Vayadande, K. B., Bodhankar, A., Mahajan, A., Prasad, D., Mahajan, S., Pujari, A., & Dhakalkar, R. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20225001005 · Cited by 7 · see citing works
- Authors
- Cacheda, F., Fernandez, D., Novoa, F. J., & Carneiro, V.
- Affiliations
- Universidade da Coruña (ES)
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2019-06-10
- Volume
- 21
- Issue
- 6
- Pages
- e12554
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6598420
- OpenAlex
- W2927590866
- Cited by
- 206
- References
- 39
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 40 · 2023: 39 · 2022: 33 · 2021: 34 · 2020: 17
- FWCI
- 26.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- RUC (Universidade Da Coruña) (submittedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- depression detection, major depressive disorder, early detection, social media, social network analysis, random forest, text features, semantic similarity, writing style
- MeSH terms
- Machine Learning, Artificial Intelligence, Depression, Female, Humans, Male, Remote Sensing Technology, Social Networking
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