Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, March 11, 2019 at 4:00 PM to 4:30 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

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

Published on 24.10.16 in Vol 2, No 2 (2016): Jul-Dec

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

Works citing "“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets"

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.6327):

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

  1. Guiñazú MF, Cortés V, Ibáñez CF, Velásquez JD. Employing online social networks in precision-medicine approach using information fusion predictive model to improve substance use surveillance: A lesson from Twitter and marijuana consumption. Information Fusion 2020;55:150
    CrossRef
  2. Adams N, Artigiani EE, Wish ED. Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List. Journal of Drug Issues 2019;49(3):477
    CrossRef
  3. Ashford RD, Curtis BL. Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER , 2019). Alcoholism: Clinical and Experimental Research 2019;43(3):401
    CrossRef
  4. Mamidi R, Miller M, Banerjee T, Romine W, Sheth A. Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic. JMIR Public Health and Surveillance 2019;5(2):e11036
    CrossRef
  5. 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
  6. 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
  7. 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
  8. Lamy FR, Daniulaityte R, Zatreh M, Nahhas RW, Sheth A, Martins SS, Boyer EW, Carlson RG. "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates. Drug and Alcohol Dependence 2018;183:248
    CrossRef
  9. 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
  10. 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
    CrossRef
  11. Daniulaityte R, Lamy FR, Barratt M, Nahhas RW, Martins SS, Boyer EW, Sheth A, Carlson RG. Characterizing marijuana concentrate users: A web-based survey. Drug and Alcohol Dependence 2017;178:399
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.6327)

:
  1. Zhang H, Wheldon C, Tao C, Dunn AG, Guo Y, Huo J, Bian J. Social Web and Health Research. 2019. Chapter 11:207
    CrossRef
  2. Natsiavas P, Maglaveras N, Koutkias V. Knowledge Representation for Health Care. 2017. Chapter 4:51
    CrossRef