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

Journals

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  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
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  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
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  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
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  29. Dias L, Vianna H, Barbosa J. Human behaviour data analysis and noncommunicable diseases: a systematic mapping study. Behaviour & Information Technology 2023;42(14):2485 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 2023;42(32):27901 View
  34. Tejaswini V, Sathya 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 2024;23(1):1 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
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  40. Shi J, Khoo Z. Online health community for change: Analysis of self-disclosure and social networks of users with depression. Frontiers in Psychology 2023;14 View
  41. Pool-Cen J, Carlos-Martínez H, Hernández-Chan G, Sánchez-Siordia O. Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation. Healthcare 2023;11(7):1057 View
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  44. Aleman-Zambrano G, Del Carpio-Lazo M, Mendiguri-Chávez D, Vilchez-Silva D, Tejada Toledo F. Modelo de clasificación de depresión en Tweets usando BERT. Innovación y Software 2023;4(2):6 View
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  46. Shi J, Khoo Z. Words for the hearts: a corpus study of metaphors in online depression communities. Frontiers in Psychology 2023;14 View
  47. Villa-Pérez M, Trejo L, Moin M, Stroulia E. Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach. IEEE Access 2023;11:128135 View
  48. Xu C, Wongpakaran N, Wongpakaran T, Siriwittayakorn T, Wedding D, Varnado P. Syntactic Errors in Older Adults with Depression. Medicina 2023;59(12):2133 View
  49. Królak A, Wiktorski T, Żmudzińska A. Automatic analysis of X (Twitter) data for supporting depression diagnosis. Human Technology 2023;19(3):370 View
  50. 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
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  52. Salas-Zárate R, Alor-Hernández G, Paredes-Valverde M, Salas-Zárate M, Bustos-López M, Sánchez-Cervantes J. Mental-Health: An NLP-Based System for Detecting Depression Levels through User Comments on Twitter (X). Mathematics 2024;12(13):1926 View
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Books/Policy Documents

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