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

Infodemiology and Infoveillance (1007) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1275) Depression and Mood Disorders; Suicide Prevention (1032)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14199, first published .
Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

Authors of this article:

Angela Leis 1 Author Orcid Image ;   Francesco Ronzano 1 Author Orcid Image ;   Miguel A Mayer 1 Author Orcid Image ;   Laura I Furlong 1 Author Orcid Image ;   Ferran Sanz 1 Author Orcid Image
Article Authors Cited by (44) Tweetations (80) Metrics

Journals

  1. 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
  2. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, Zheng X, Wang T, Lei J. Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed. Journal of Medical Internet Research 2020;22(1):e16816 View
  3. Garcia-Rudolph A, Saurí J, Cegarra B, Bernabeu Guitart M. Discovering the Context of People With Disabilities: Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit. JMIR Medical Informatics 2020;8(11):e17903 View
  4. Ávila-Tomás J, Mayer-Pujadas M, Quesada-Varela V. La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas. Atención Primaria 2021;53(1):81 View
  5. Garg S, Raigosa A, Aiman R. Investigating differential linguistic patterns exhibited by Major Depressive Disorder (MDD) Patients and building a Long Short Term Memory Network + Convolutional Neural Network Model, Logistic Regression model, and a Multinomial Naive Bayes Classifier Algorithm to develop Spero, a hybrid app based Early-MDD diagnosis system. International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2020:114 View
  6. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study. Journal of Medical Internet Research 2020;22(12):e20920 View
  7. Merz A, Gutiérrez-Sacristán A, Bartz D, Williams N, Ojo A, Schaefer K, Huang M, Li C, Sandoval R, Ye S, Cathcart A, Starosta A, Avillach P. Population attitudes toward contraceptive methods over time on a social media platform. American Journal of Obstetrics and Gynecology 2021;224(6):597.e1 View
  8. Kelly D, Spaderna M, Hodzic V, Nair S, Kitchen C, Werkheiser A, Powell M, Liu F, Coppersmith G, Chen S, Resnik P. Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls. Psychiatry Research 2020;294:113496 View
  9. Wen S. Detecting Depression from Tweets with Neural Language Processing. Journal of Physics: Conference Series 2021;1792(1):012058 View
  10. HUANG G, ZHOU X. The linguistic patterns of depressed patients. Advances in Psychological Science 2021;29(5):838 View
  11. Ramírez-Cifuentes D, Freire A, Baeza-Yates R, Sanz Lamora N, Álvarez A, González-Rodríguez A, Lozano Rochel M, Llobet Vives R, Velazquez D, Gonfaus J, Gonzàlez J. Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis. Journal of Medical Internet Research 2021;23(7):e25925 View
  12. Cohrdes C, Yenikent S, Wu J, Ghanem B, Franco-Salvador M, Vogelgesang F. Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data. JMIR Mental Health 2021;8(6):e27140 View
  13. Zhou J, Zogan H, Yang S, Jameel S, Xu G, Chen F. Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia. IEEE Transactions on Computational Social Systems 2021;8(4):982 View
  14. Wongkoblap A, Vadillo M, Curcin V. Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study. JMIR Mental Health 2021;8(8):e19824 View
  15. Cuerda C, Zornoza A, Gallud J, Tesoriero R, Ayuso D. Deep learning assisted cognitive diagnosis for the D-Riska application. Soft Computing 2022;26(2):665 View
  16. Koops S, Brederoo S, de Boer J, Nadema F, Voppel A, Sommer I. Speech as a Biomarker for Depression. CNS & Neurological Disorders - Drug Targets 2023;22(2):152 View
  17. Sierra G, Andrade-Palos P, Bel-Enguix G, Osornio-Arteaga A, Cabrera-Mora A, García-Nieto L, Sierra-Aparicio T. Suicide Risk Factors: A Language Analysis Approach in Social Media. Journal of Language and Social Psychology 2022;41(3):312 View
  18. Kelley S, Gillan C. Using language in social media posts to study the network dynamics of depression longitudinally. Nature Communications 2022;13(1) View
  19. Han J, Feng Y, Li N, Feng L, Xiao L, Zhu X, Wang G. Correlation Between Word Frequency and 17 Items of Hamilton Scale in Major Depressive Disorder. Frontiers in Psychiatry 2022;13 View
  20. Kelley S, Mhaonaigh C, Burke L, Whelan R, Gillan C. Machine learning of language use on Twitter reveals weak and non-specific predictions. npj Digital Medicine 2022;5(1) View
  21. Pilipiec P, Samsten I, Bota A, Rocha L. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101 View
  22. Razia Sulthana A. , Jaithunbi A. K. , Harikrishnan H, Varadarajan V. Sentiment Analysis on Movie Reviews Dataset Using Support Vector Machines and Ensemble Learning. International Journal of Information Technology and Web Engineering 2022;17(1):1 View
  23. Salas-Zárate R, Alor-Hernández G, Salas-Zárate M, Paredes-Valverde M, Bustos-López M, Sánchez-Cervantes J. Detecting Depression Signs on Social Media: A Systematic Literature Review. Healthcare 2022;10(2):291 View
  24. Savekar A, Tarai S, Singh M. Structural and functional markers of language signify the symptomatic effect of depression: A systematic literature review. European Journal of Applied Linguistics 2023;11(1):190 View
  25. Zhang Y, Lyu H, Liu Y, Zhang X, Wang Y, Luo J. Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study. JMIR Infodemiology 2021;1(1):e26769 View
  26. 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
  27. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(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 2023 View
  29. Dias L, Vianna H, Barbosa J. Human behaviour data analysis and noncommunicable diseases: a systematic mapping study. Behaviour & Information Technology 2022:1 View
  30. 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
  31. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266 View
  32. Sheoran H, Srivastava P. Self-Reported Depression Is Associated With Aberration in Emotional Reactivity and Emotional Concept Coding. Frontiers in Psychology 2022;13 View
  33. Pan W, Han Y, Li J, Zhang E, He B. The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model. Current Psychology 2022 View
  34. Tejaswini V, Babu K, Sahoo B. Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model. ACM Transactions on Asian and Low-Resource Language Information Processing 2022 View
  35. Cai Y, Wang H, Ye H, Jin Y, Gao W. Depression detection on online social network with multivariate time series feature of user depressive symptoms. Expert Systems with Applications 2023;217:119538 View
  36. Abu-Taieh E, AlHadid I, Masa’deh R, Alkhawaldeh R, Khwaldeh S, Alrowwad A. Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM. International Journal of Environmental Research and Public Health 2022;19(21):13764 View
  37. A. Musleh D, A. Alkhales T, A. Almakki R, E. Alnajim S, K. Almarshad S, S. Alhasaniah R, S. Aljameel S, A. Almuqhim A. Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning. Computers, Materials & Continua 2022;71(2):3463 View
  38. Meena R, Thulasi Bai V. Depression Detection on COVID 19 Tweets Using Chimp Optimization Algorithm. Intelligent Automation & Soft Computing 2022;34(3):1643 View
  39. Barreto L, Freitas V, Paula V. Emotional branding e engajamento do consumidor em tempos de pandemia em redes sociais. Revista Eletrônica de Ciência Administrativa 2023;22(1):112 View

Books/Policy Documents

  1. Zhao Y, Prosperi M, Lyu T, Guo Y, Zhou L, Bian J. Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. View
  2. Varga J, Lezama O, Payares K. Proceedings of International Conference on Intelligent Computing, Information and Control Systems. View