RefCheck Maintenance Notice

On Monday, December 3, 2018, from 16:00-18:00 EST, RefCheck will be undergoing maintenance. RefCheck is the process where, during copyediting, all references are extracted from the manuscript file, parsed, matched against various databases (eg, PubMed and CrossRef), and automatically corrected. For more information on RefCheck, please visit our Knowledge Base.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 12.05.15 in Vol 2, No 2 (2015): April-June

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

Works citing "Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model"

According to Crossref, the following articles are citing this article (DOI 10.2196/mental.4227):

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

  1. 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
  2. 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
  3. Fodeh SJ, Boudreaux ED, Wang R, Silva D, Bossarte R, Goulet JL, Brandt C, Altalib HH. Suicide Risk on Twitter. International Journal of Knowledge Discovery in Bioinformatics 2018;8(2):1
    CrossRef
  4. Taylor J, Pagliari C. Mining social media data: How are research sponsors and researchers addressing the ethical challenges?. Research Ethics 2018;14(2):1
    CrossRef
  5. Li A, Jiao D, Zhu T. Detecting depression stigma on social media: A linguistic analysis. Journal of Affective Disorders 2018;232:358
    CrossRef
  6. 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
  7. Liu LL, Li TM, Teo AR, Kato TA, Wong PW. Harnessing Social Media to Explore Youth Social Withdrawal in Three Major Cities in China: Cross-Sectional Web Survey. JMIR Mental Health 2018;5(2):e34
    CrossRef
  8. 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
  9. Hettige NC, Nguyen TB, Yuan C, Rajakulendran T, Baddour J, Bhagwat N, Bani-Fatemi A, Voineskos AN, Mallar Chakravarty M, De Luca V. Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach. General Hospital Psychiatry 2017;47:20
    CrossRef
  10. 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
  11. Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL. Validating Machine Learning Algorithms for Twitter Data Against Established Measures of Suicidality. JMIR Mental Health 2016;3(2):e21
    CrossRef
  12. Cheng Q, Kwok C, Zhu T, Guan L, Yip P. Suicide Communication on Social Media and Its Psychological Mechanisms: An Examination of Chinese Microblog Users. International Journal of Environmental Research and Public Health 2015;12(9):11506
    CrossRef