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Citing this Article

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Published on 28.02.17 in Vol 19, No 2 (2017): February

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

Works citing "Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study"

Danielle Mowery, Hilary Smith, Tyler Cheney, Greg Stoddard, Glen Coppersmith, Craig Bryan, Mike Conway

J Med Internet Res 2017 (Feb 28); 19(2):e48 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.6895):

(note that this is only a small subset of citations)

  1. Dreisbach C, Koleck TA, Bourne PE, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International Journal of Medical Informatics 2019;125:37
    CrossRef
  2. Todorov G, Mayilvahanan K, Cain C, Cunha C. Context- and Subgroup-Specific Language Changes in Individuals Who Develop PTSD After Trauma. Frontiers in Psychology 2020;11
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  3. Leis A, Ronzano F, Mayer MA, Furlong LI, Sanz F. Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research 2019;21(6):e14199
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  4. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth CM. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550
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  5. Booth JM, Lin Y, Wei K. Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects. Journal of Community Psychology 2018;46(7):829
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  6. Acuña Caicedo RW, Gómez Soriano JM, Melgar Sasieta HA. Assessment of supervised classifiers for the task of detecting messages with suicidal ideation. Heliyon 2020;6(8):e04412
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  7. Kim SJ, Marsch LA, Hancock JT, Das AK. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353
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  8. Martinez LS, Hughes S, Walsh-Buhi ER, Tsou M. “Okay, We Get It. You Vape”: An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter. Journal of Health Communication 2018;23(6):550
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  9. Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
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  10. Calvo RA, Dinakar K, Picard R, Christensen H, Torous J. Toward Impactful Collaborations on Computing and Mental Health. Journal of Medical Internet Research 2018;20(2):e49
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  11. Doan S, Yang EW, Tilak SS, Li PW, Zisook DS, Torii M. Extracting health-related causality from twitter messages using natural language processing. BMC Medical Informatics and Decision Making 2019;19(S3)
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  12. Barros JM, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680
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  13. Seabrook EM, Kern ML, Fulcher BD, Rickard NS. Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates. Journal of Medical Internet Research 2018;20(5):e168
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  14. Sampson JP, Kettunen J, Vuorinen R. The role of practitioners in helping persons make effective use of information and communication technology in career interventions. International Journal for Educational and Vocational Guidance 2020;20(1):191
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  15. Hou Y, Liu Y, Liu C, Yan Z, Ma Q, Chen J, Zhang M, Yan Q, Li X, Chen J. Xiaoyaosan regulates depression‐related behaviors with physical symptoms by modulating Orexin A/OxR1 in the hypothalamus. The Anatomical Record 2020;303(8):2144
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  16. . Social Media as a Catalyst for Policy Action and Social Change for Health and Well-Being: Viewpoint. Journal of Medical Internet Research 2018;20(3):e94
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  17. Velupillai S, Suominen H, Liakata M, Roberts A, Shah AD, Morley K, Osborn D, Hayes J, Stewart R, Downs J, Chapman W, Dutta R. Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances. Journal of Biomedical Informatics 2018;88:11
    CrossRef
  18. Yao X, Yu G, Tang J, Zhang J. Extracting depressive symptoms and their associations from an online depression community. Computers in Human Behavior 2021;120:106734
    CrossRef
  19. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1
    CrossRef
  20. Kelly DL, Spaderna M, Hodzic V, Nair S, Kitchen C, Werkheiser AE, Powell MM, 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
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  21. Tao X, Chi O, Delaney PJ, Li L, Huang J. Detecting depression using an ensemble classifier based on Quality of Life scales. Brain Informatics 2021;8(1)
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  22. Athira B, Jones J, Idicula SM, Kulanthaivel A, Zhang E. Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study. Journal of Big Data 2021;8(1)
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  23. Le Glaz A, Haralambous Y, Kim-Dufor D, Lenca P, Billot R, Ryan TC, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. Journal of Medical Internet Research 2021;23(5):e15708
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  24. Garske SI, Elayan S, Sykora M, Edry T, Grabenhenrich LB, Galea S, Lowe SR, Gruebner O. Space-Time Dependence of Emotions on Twitter after a Natural Disaster. International Journal of Environmental Research and Public Health 2021;18(10):5292
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  25. 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
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  26. Viviani M, Crocamo C, Mazzola M, Bartoli F, Carrà G, Pasi G. Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content. Future Generation Computer Systems 2021;125:446
    CrossRef
  27. Rook L, Mazza MC, Lefter I, Brazier F. Toward Linguistic Recognition of Generalized Anxiety Disorder. Frontiers in Digital Health 2022;4
    CrossRef
  28. Galbraith E, Li J, Rio-Vilas VJD, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Scientific Reports 2022;12(1)
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  29. FOWLER JC, MADAN A, BRUCE CR, FRUEH BC, KASH B, JONES SL, SASANGOHAR F. Improving Psychiatric Care Through Integrated Digital Technologies. Journal of Psychiatric Practice 2021;27(2):92
    CrossRef
  30. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266
    CrossRef
  31. 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
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  32. Blanco G, Lourenço A. Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations. Information Processing & Management 2022;59(3):102918
    CrossRef
  33. Acuña Caicedo RW, Gómez Soriano JM, Melgar Sasieta HA. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519
    CrossRef
  34. Noraset T, Chatrinan K, Tawichsri T, Thaipisutikul T, Tuarob S. Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks. Journal of Biomedical Informatics 2022;133:104145
    CrossRef
  35. Kang J, Kim J, Kim T, Song H, Han J. Experiencing Stress During COVID-19: A Computational Analysis of Stressors and Emotional Responses to Stress. Cyberpsychology, Behavior, and Social Networking 2022;25(9):561
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  36. Shakeri Hossein Abad Z, Butler GP, Thompson W, Lee J. Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research. JMIR Public Health and Surveillance 2022;8(2):e32355
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  37. Gallegos Salazar LM, Loyola-González O, Medina-Pérez MA. An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks. Applied Sciences 2021;11(22):10932
    CrossRef
  38. Pilipiec P, Samsten I, Bota A, Rocha LM. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101
    CrossRef
  39. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1)
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  40. Nanath K, Balasubramanian S, Shukla V, Islam N, Kaitheri S. Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic. Technological Forecasting and Social Change 2022;178:121560
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  41. Rezaeian A, Agha Akbari L, Amirzadeh F, MalekMohammadi N. The Effectiveness of Acceptance and Commitment Therapy on Distress Tolerance and Depression in Students. Quarterly Journal of Child Mental Health 2021;8(4):94
    CrossRef
  42. Eaton MC, Probst YC, Smith MA. Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis. JMIR Infodemiology 2023;3:e38245
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  43. Norman P, Pickering CM. Discourse about national parks on social media: Insights from Twitter. Journal of Outdoor Recreation and Tourism 2023;44:100682
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  44. Zarate D, Ball M, Prokofieva M, Kostakos V, Stavropoulos V. Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Research 2023;330:115579
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  45. Zhang J, Xu W, Lei C, Pu Y, Zhang Y, Zhang J, Yu H, Su X, Huang Y, Gong R, Zhang L, Shi Q. Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study. JMIR Formative Research 2023;7:e43995
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  46. 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
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  47. Królak A, Wiktorski T, Żmudzińska A. Automatic analysis of X (Twitter) data for supporting depression diagnosis. Human Technology 2023;19(3):370
    CrossRef
  48. Wu X, Zhou Y, Zhong B. Measuring social support for depression on social media: A multifaceted study on user interaction and emotional spread. Telematics and Informatics 2024;89:102120
    CrossRef
  49. Thakkar A, Gupta A, De Sousa A. Artificial intelligence in positive mental health: a narrative review. Frontiers in Digital Health 2024;6
    CrossRef
  50. Farruque N, Goebel R, Sivapalan S, Zaïane OR. Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach. Language Resources and Evaluation 2024;
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.6895):

  1. Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. 2019. Chapter 29:583
    CrossRef
  2. Ismail NH, Du M, Hu X. Social Web and Health Research. 2019. Chapter 9:171
    CrossRef
  3. Razak CSA, Zulkarnain MA, Hamid SHA, Anuar NB, Jali MZ, Meon H. Computational Science and Technology. 2020. Chapter 52:543
    CrossRef
  4. Dey J, Sarkar A, Karforma S. Recent Trends in Computational Intelligence Enabled Research. 2021. :317
    CrossRef
  5. Ingram WM, Khanna R, Weston C. Mental Health Informatics. 2021. Chapter 17:453
    CrossRef
  6. Usharani B, Goyal LM. Predictive Analytics of Psychological Disorders in Healthcare. 2022. Chapter 13:253
    CrossRef
  7. Thakur N, Cho H, Cheng H, Lee H. HCI International 2023 – Late Breaking Papers. 2023. Chapter 27:367
    CrossRef