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Citing this Article

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Published on 16.05.16 in Vol 3, No 2 (2016): Apr-Jun

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

Works citing "Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality"

Scott R. Braithwaite, Christophe Giraud-Carrier, Josh West, Michael D. Barnes, Carl Lee Hanson

JMIR Ment Health 2016 (May 16); 3(2):e21 

According to Crossref, the following articles are citing this article (DOI 10.2196/mental.4822):

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

  1. Ortiz P, Khin Khin E. Traditional and new media's influence on suicidal behavior and contagion. Behavioral Sciences & the Law 2018;36(2):245
    CrossRef
  2. Cheng Q, Li TM, Kwok C, Zhu T, Yip PS. Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study. Journal of Medical Internet Research 2017;19(7):e243
    CrossRef
  3. Carreiro S, Chai PR, Carey J, Chapman B, Boyer EW. Integrating Personalized Technology in Toxicology: Sensors, Smart Glass, and Social Media Applications in Toxicology Research. Journal of Medical Toxicology 2017;13(2):166
    CrossRef
  4. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
    CrossRef
  5. 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)
    CrossRef
  6. Soucy JN, Hadjistavropoulos HD, Couture CA, Owens VA, Dear BF, Titov N. Content of client emails in internet-delivered cognitive behaviour therapy: A comparison between two trials and relationship to client outcome. Internet Interventions 2018;11:53
    CrossRef
  7. Grant RN, Kucher D, León AM, Gemmell JF, Raicu DS, Fodeh SJ. Automatic extraction of informal topics from online suicidal ideation. BMC Bioinformatics 2018;19(S8)
    CrossRef
  8. Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. International Journal of Environmental Research and Public Health 2020;17(16):5929
    CrossRef
  9. Chancellor S, Baumer EPS, De Choudhury M. Who is the "Human" in Human-Centered Machine Learning. Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1
    CrossRef
  10. Colditz JB, Chu K, Emery SL, Larkin CR, James AE, Welling J, Primack BA. Toward Real-Time Infoveillance of Twitter Health Messages. American Journal of Public Health 2018;108(8):1009
    CrossRef
  11. Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. npj Digital Medicine 2020;3(1)
    CrossRef
  12. 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
    CrossRef
  13. Glenn JJ, Nobles AL, Barnes LE, Teachman BA. Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally Sensitive Markers of Suicide Risk. Clinical Psychological Science 2020;8(4):704
    CrossRef
  14. Spates K, Ye X, Johnson A. “I just might kill myself”: Suicide expressions on Twitter. Death Studies 2020;44(3):189
    CrossRef
  15. Cohan A, Young S, Yates A, Goharian N. Triaging content severity in online mental health forums. Journal of the Association for Information Science and Technology 2017;68(11):2675
    CrossRef
  16. Coşkun M, Ozturan M. #europehappinessmap: A Framework for Multi-Lingual Sentiment Analysis via Social Media Big Data (A Twitter Case Study). Information 2018;9(5):102
    CrossRef
  17. Walters K, Christakis DA, Wright DR, Alamian A. Are Mechanical Turk worker samples representative of health status and health behaviors in the U.S.?. PLOS ONE 2018;13(6):e0198835
    CrossRef
  18. Oexle N, Niederkrotenthaler T, DeLeo D. Emerging trends in suicide prevention research. Current Opinion in Psychiatry 2019;32(4):336
    CrossRef
  19. Bruen AJ, Wall A, Haines A, Perkins E. The practicalities and acceptability of setting up a trial involving mobile technology and mental health service users: exploring suicidal ideation using an innovative mobile app - Strength Within Me (SWiM) (Preprint). JMIR Mental Health 2020;
    CrossRef
  20. Priya A, Garg S, Tigga NP. Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms. Procedia Computer Science 2020;167:1258
    CrossRef
  21. Brown RC, Bendig E, Fischer T, Goldwich AD, Baumeister H, Plener PL, Harris KM. Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses. PLOS ONE 2019;14(9):e0220623
    CrossRef
  22. Aladağ AE, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO. Detecting Suicidal Ideation on Forums: Proof-of-Concept Study. Journal of Medical Internet Research 2018;20(6):e215
    CrossRef
  23. Tadesse MM, Lin H, Xu B, Yang L. Detection of Suicide Ideation in Social Media Forums Using Deep Learning. Algorithms 2019;13(1):7
    CrossRef
  24. Barnes M, Hanson C, Giraud-Carrier C. The Case for Computational Health Science. Journal of Healthcare Informatics Research 2018;2(1-2):99
    CrossRef
  25. Haines-Delmont A, Chahal G, Bruen AJ, Wall A, Khan CT, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR mHealth and uHealth 2020;8(6):e15901
    CrossRef
  26. , , , , . Commentary: A time to reap and a time to sow: reducing the adolescent suicide rate now and in the future: commentary on Cha et al. (2018). Journal of Child Psychology and Psychiatry 2018;59(4):483
    CrossRef
  27. Lopez‐Castroman J, Moulahi B, Azé J, Bringay S, Deninotti J, Guillaume S, Baca‐Garcia E. Mining social networks to improve suicide prevention: A scoping review. Journal of Neuroscience Research 2020;98(4):616
    CrossRef
  28. Wongkoblap A, Vadillo MA, Curcin V. Researching Mental Health Disorders in the Era of Social Media: Systematic Review. Journal of Medical Internet Research 2017;19(6):e228
    CrossRef
  29. Niederkrotenthaler T, Till B, Garcia D. Celebrity suicide on Twitter: Activity, content and network analysis related to the death of Swedish DJ Tim Bergling alias Avicii. Journal of Affective Disorders 2019;245:848
    CrossRef
  30. King CA, Arango A, Ewell Foster C. Emerging trends in adolescent suicide prevention research. Current Opinion in Psychology 2018;22:89
    CrossRef
  31. , , , , . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
    CrossRef
  32. Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media 2017;2:32
    CrossRef
  33. Chiang C, Kasunic A, Savage S. Crowd Coach. Proceedings of the ACM on Human-Computer Interaction 2018;2(CSCW):1
    CrossRef
  34. Kornfield R, Sarma PK, Shah DV, McTavish F, Landucci G, Pe-Romashko K, Gustafson DH. Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum. Journal of Medical Internet Research 2018;20(6):e10136
    CrossRef
  35. Melia R, Francis K, Duggan J, Bogue J, O'Sullivan M, Chambers D, Young K. Mobile Health Technology Interventions for Suicide Prevention: Protocol for a Systematic Review and Meta-Analysis. JMIR Research Protocols 2018;7(1):e28
    CrossRef
  36. Burke TA, Ammerman BA, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. Journal of Affective Disorders 2019;245:869
    CrossRef
  37. Sabus C, Johns B, Schultz N, Gagnon K. Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter. Physical Therapy 2019;99(8):1048
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mental.4822):

  1. Lawrence D, Carrington-Jones P, Kyron MJ. Alternatives to Suicide. 2020. :221
    CrossRef
  2. Liang C, Abbott D, Hong YA, Madadi M, White A. Social Computing and Social Media. Design, Human Behavior and Analytics. 2019. Chapter 25:345
    CrossRef
  3. Roza TH, Patusco LM, Zimerman A, Ballester P, Passos IC. Precision Medicine for Investigators, Practitioners and Providers. 2020. :331
    CrossRef
  4. Adrian M, Lyon AR. Technology and Adolescent Mental Health. 2018. Chapter 12:155
    CrossRef
  5. Kessler RC, Bernecker SL, Bossarte RM, Luedtke AR, McCarthy JF, Nock MK, Pigeon WR, Petukhova MV, Sadikova E, VanderWeele TJ, Zuromski KL, Zaslavsky AM. Personalized Psychiatry. 2019. Chapter 5:77
    CrossRef
  6. Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. 2019. Chapter 29:583
    CrossRef
  7. Kamarudin NS, Beigi G, Manikonda L, Liu H. Open Source Intelligence and Cyber Crime. 2020. Chapter 8:195
    CrossRef