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

Infodemiology and Infoveillance (1274) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1570) Depression and Mood Disorders; Suicide Prevention (1282) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1013)

Published on in Vol 20, No 5 (2018): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9267, first published .
Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

Authors of this article:

Elizabeth M Seabrook1 Author Orcid Image ;   Margaret L Kern2 Author Orcid Image ;   Ben D Fulcher1 Author Orcid Image ;   Nikki S Rickard1, 2 Author Orcid Image
Article Authors Cited by (64) Tweetations (25) Metrics

Journals

  1. Cheng T, Liu L, Woo B. Analyzing Twitter as a Platform for Alzheimer-Related Dementia Awareness: Thematic Analyses of Tweets. JMIR Aging 2018;1(2):e11542 View
  2. Кисельникова Н, Куминская Е, Латышев А, Фраленко В, Хачумов М, Khachumov V. Tools for the analysis of the depressed state and personality traits of a person. Program Systems: Theory and Applications 2019;10(3):129 View
  3. Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digital Medicine 2020;3(1) View
  4. Ricard B, Marsch L, Crosier B, Hassanpour S. Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram. Journal of Medical Internet Research 2018;20(12):e11817 View
  5. Cole D, Nick E, Varga G, Smith D, Zelkowitz R, Ford M, Lédeczi Á. Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support. Cyberpsychology, Behavior, and Social Networking 2019;22(11):692 View
  6. Burdick L, Mihalcea R, Boyd R, Pennebaker J. Analyzing Connections Between User Attributes, Images, and Text. Cognitive Computation 2021;13(2):241 View
  7. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research 2019;21(6):e14199 View
  8. Danina M, Kiselnikova N, Kuminskaya E, Lavrova E, Greskova P. Methods for Preventing Depression on Digital Platforms and in Social Media. Clinical Psychology and Special Education 2019;8(3):101 View
  9. Giuntini F, Cazzolato M, dos Reis M, Campbell A, Traina A, Ueyama J. A review on recognizing depression in social networks: challenges and opportunities. Journal of Ambient Intelligence and Humanized Computing 2020;11(11):4713 View
  10. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth C. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550 View
  11. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  12. Tang J, Yu G, Yao X. A Comparative Study of Online Depression Communities in China. International Journal of Environmental Research and Public Health 2020;17(14):5023 View
  13. Marsch L. Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology 2021;46(1):191 View
  14. Yao X, Yu G, Tian X, Tang J. Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo. Telemedicine and e-Health 2020;26(6):734 View
  15. Stirling E, Willcox J, Ong K, Forsyth A. Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours. Public Health Nutrition 2021;24(6):1193 View
  16. Dwyer A, de Almeida Neto A, Estival D, Li W, Lam-Cassettari C, Antoniou M. Suitability of Text-Based Communications for the Delivery of Psychological Therapeutic Services to Rural and Remote Communities: Scoping Review. JMIR Mental Health 2021;8(2):e19478 View
  17. Kim J, Uddin Z, Lee Y, Nasri F, Gill H, Subramanieapillai M, Lee R, Udovica A, Phan L, Lui L, Iacobucci M, Mansur R, Rosenblat J, McIntyre R. A Systematic review of the validity of screening depression through Facebook, Twitter, Instagram, and Snapchat. Journal of Affective Disorders 2021;286:360 View
  18. Linton M, Jelbert S, Kidger J, Morris R, Biddle L, Hood B. Investigating the Use of Electronic Well-being Diaries Completed Within a Psychoeducation Program for University Students: Longitudinal Text Analysis Study. Journal of Medical Internet Research 2021;23(4):e25279 View
  19. O’Dea B, Boonstra T, Larsen M, Nguyen T, Venkatesh S, Christensen H, Boyd R. The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study. PLOS ONE 2021;16(5):e0251787 View
  20. Dysthe K, Haavet O, Røssberg J, Brandtzaeg P, Følstad A, Klovning A. Finding Relevant Psychoeducation Content for Adolescents Experiencing Symptoms of Depression: Content Analysis of User-Generated Online Texts. Journal of Medical Internet Research 2021;23(9):e28765 View
  21. 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
  22. Lane J, Habib D, Curtis B. Linguistic Methodologies to Surveil the Leading Causes of Mortality: Scoping Review of Twitter for Public Health Data. Journal of Medical Internet Research 2023;25:e39484 View
  23. Sakib A, Mukta M, Huda F, Islam A, Islam T, Ali M. Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets. Journal of Medical Internet Research 2021;23(12):e27613 View
  24. Antoniou M, Estival D, Lam-Cassettari C, Li W, Dwyer A, Neto A. Predicting Mental Health Status in Remote and Rural Farming Communities: Computational Analysis of Text-Based Counseling. JMIR Formative Research 2022;6(6):e33036 View
  25. 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
  26. Bioglio L, Pensa R. Analysis and classification of privacy-sensitive content in social media posts. EPJ Data Science 2022;11(1) View
  27. Alavijeh S, Zarrinkalam F, Noorian Z, Mehrpour A, Etminani K. What users’ musical preference on Twitter reveals about psychological disorders. Information Processing & Management 2023;60(3):103269 View
  28. Bowling J, Montanaro E, Ordonez S, McCabe S, Farris S, Saint-Cyr N, Glaser W, Cramer R, Langhinrichsen-Rohling J, Mennicke A, Al-Yateem N. Coming together in a digital age: Community twitter responses in the wake of a campus shooting. PLOS ONE 2022;17(12):e0279569 View
  29. 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
  30. Bettis A, Burke T, Nesi J, Liu R. Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clinical Psychological Science 2022;10(1):3 View
  31. 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
  32. Dysthe K, Røssberg J, Brandtzaeg P, Skjuve M, Haavet O, Følstad A, Klovning A. Analyzing User-Generated Web-Based Posts of Adolescents’ Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis. Journal of Medical Internet Research 2023;25:e37289 View
  33. Hao F, Park E, Chon K. Social Media and Disaster Risk Reduction and Management: How Have Reddit Travel Communities Experienced the COVID-19 Pandemic?. Journal of Hospitality & Tourism Research 2024;48(1):58 View
  34. Montag C, Dagum P, Hall B, Elhai J. Do we still need psychological self-report questionnaires in the age of the Internet of Things?. Discover Psychology 2022;2(1) View
  35. Hänsel K, Lin I, Sobolev M, Muscat W, Yum-Chan S, De Choudhury M, Kane J, Birnbaum M. Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders. Frontiers in Psychiatry 2021;12 View
  36. 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
  37. Kumar A, Quadir Md A, Christy Jackson J, Iwendi C. Predicting and Curing Depression Using Long Short Term Memory and Global Vector. Computers, Materials & Continua 2023;74(3):5837 View
  38. Ragheb W, Aze J, Bringay S, Servajean M. Negatively Correlated Noisy Learners for At-risk User Detection on Social Networks: A Study on Depression, Anorexia, Self-harm and Suicide. IEEE Transactions on Knowledge and Data Engineering 2021:1 View
  39. Giuntini F, de Moraes K, Cazzolato M, Kirchner L, Dos Reis M, Traina A, Campbell A, Ueyama J. Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining. IEEE Access 2021;9:97621 View
  40. 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
  41. Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?. Psychologische Rundschau 2023;74(2):89 View
  42. Guo Z, Ding N, Zhai M, Zhang Z, Li Z. Leveraging Domain Knowledge to Improve Depression Detection on Chinese Social Media. IEEE Transactions on Computational Social Systems 2023;10(4):1528 View
  43. Guo Y, Li Y, Liu D, Xu S. Measuring service quality based on customer emotion: An explainable AI approach. Decision Support Systems 2024;176:114051 View
  44. Obagbuwa I, Danster S, Chibaya O. Supervised machine learning models for depression sentiment analysis. Frontiers in Artificial Intelligence 2023;6 View
  45. Allen K, Davis A, Krishnamurti T. Indirect Identification of Perinatal Psychosocial Risks From Natural Language. IEEE Transactions on Affective Computing 2023;14(2):1506 View
  46. Farruque N, Goebel R, Sivapalan S, Zaïane O. Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach. Language Resources and Evaluation 2024 View
  47. Ferrario A, Sedlakova J, Trachsel M. The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis. JMIR Mental Health 2024;11:e56569 View

Books/Policy Documents

  1. Stankevich M, Smirnov I, Kiselnikova N, Ushakova A. Data Analytics and Management in Data Intensive Domains. View
  2. Marechal C, Mikołajewski D, Tyburek K, Prokopowicz P, Bougueroua L, Ancourt C, Węgrzyn-Wolska K. High-Performance Modelling and Simulation for Big Data Applications. View
  3. Chen L, Magdy W, Whalley H, Wolters M. Social Informatics. View
  4. Shekerbekova S, Yerekesheva M, Tukenova L, Turganbay K, Kozhamkulova Z, Omarov B. Advanced Informatics for Computing Research. View
  5. Heinz M, Thomas N, Nguyen N, Griffin T, Jacobson N. Comprehensive Clinical Psychology. View
  6. Ingram W, Khanna R, Weston C. Mental Health Informatics. View
  7. Madera-Torres I, Orozco-del-Castillo M, Moreno-Cimé S, Bermejo-Sabbagh C, Cuevas-Cuevas N. Telematics and Computing. View
  8. Gupta U, Jain V. Emotional AI and Human-AI Interactions in Social Networking. View
  9. Chatterjee M, Modak S, Sarkar D. Cognitive Cardiac Rehabilitation Using IoT and AI Tools. View