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Citing this Article

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Published on 02.04.15 in Vol 17, No 4 (2015): April

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

Works citing "The Painful Tweet: Text, Sentiment, and Community Structure Analyses of Tweets Pertaining to Pain"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.3769):

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

  1. Ryu YU. Text-Mining of Online Discourse to Characterize the Nature of Pain in Low Back Pain. Journal of The Korean Society of Physical Medicine 2019;14(3):55
    CrossRef
  2. Jayaraman PP, Forkan ARM, Morshed A, Haghighi PD, Kang Y. Healthcare 4.0: A review of frontiers in digital health. WIREs Data Mining and Knowledge Discovery 2020;10(2)
    CrossRef
  3. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
    CrossRef
  4. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
    CrossRef
  5. Tougas ME, Chambers CT, Corkum P, Robillard JM, Gruzd A, Howard V, Kampen A, Boerner KE, Hundert AS. Social Media Content About Children’s Pain and Sleep: Content and Network Analysis. JMIR Pediatrics and Parenting 2018;1(2):e11193
    CrossRef
  6. Delir Haghighi P, Kang Y, Buchbinder R, Burstein F, Whittle S. Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter. JMIR Public Health and Surveillance 2017;3(1):e4
    CrossRef
  7. Sewalk KC, Tuli G, Hswen Y, Brownstein JS, Hawkins JB. Using Twitter to Examine Web-Based Patient Experience Sentiments in the United States: Longitudinal Study. Journal of Medical Internet Research 2018;20(10):e10043
    CrossRef
  8. Lee H, McAuley JH, Hübscher M, Allen HG, Kamper SJ, Moseley GL. Tweeting back: predicting new cases of back pain with mass social media data. Journal of the American Medical Informatics Association 2016;23(3):644
    CrossRef
  9. Manganello JA, Falisi AL, Roberts KJ, Smith KC, McKenzie LB. Pediatric injury information seeking for mothers with young children: The role of health literacy and ehealth literacy. Journal of Communication in Healthcare 2016;9(3):223
    CrossRef
  10. Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43
    CrossRef
  11. Doan S, Ritchart A, Perry N, Chaparro JD, Conway M. How Do You #relax When You’re #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets. JMIR Public Health and Surveillance 2017;3(2):e35
    CrossRef
  12. Hajiabadi M, Zare H, Bobarshad H. IEDC: An integrated approach for overlapping and non-overlapping community detection. Knowledge-Based Systems 2017;123:188
    CrossRef
  13. Baumgartner P, Peiper N. Utilizing Big Data and Twitter to Discover Emergent Online Communities of Cannabis Users. Substance Abuse: Research and Treatment 2017;11:117822181771142
    CrossRef
  14. Mullins CF, ffrench-O'Carroll R, Lane J, O'Connor T. Sharing the pain: an observational analysis of Twitter and pain in Ireland. Regional Anesthesia & Pain Medicine 2020;45(8):597
    CrossRef
  15. Kim AE, Hopper T, Simpson S, Nonnemaker J, Lieberman AJ, Hansen H, Guillory J, Porter L. Using Twitter Data to Gain Insights into E-cigarette Marketing and Locations of Use: An Infoveillance Study. Journal of Medical Internet Research 2015;17(11):e251
    CrossRef
  16. Peiper NC, Baumgartner PM, Chew RF, Hsieh YP, Bieler GS, Bobashev GV, Siege C, Zarkin GA. Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in California. Journal of Medical Internet Research 2017;19(7):e236
    CrossRef
  17. Elphinston RA, Scotti Requena S, Angus D, de Andrade D, Freeman CR, Day MA. The Promotion of Policy Changes Restricting Access to Codeine Medicines on Twitter: What do National Pain Organizations Say?. The Journal of Pain 2019;
    CrossRef
  18. Metwally O, Blumberg S, Ladabaum U, Sinha SR. Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study. Journal of Medical Internet Research 2017;19(6):e200
    CrossRef
  19. 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