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Citing this Article

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Published on 08.01.18 in Vol 4, No 1 (2018): Jan-Mar

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

Works citing "Detecting Novel and Emerging Drug Terms Using Natural Language Processing: A Social Media Corpus Study"

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.7726):

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

  1. Lavertu A, Altman RB. RedMed: Extending drug lexicons for social media applications. Journal of Biomedical Informatics 2019;99:103307
  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
  3. Hu H, Phan N, Chun SA, Geller J, Vo H, Ye X, Jin R, Ding K, Kenne D, Dou D. An insight analysis and detection of drug-abuse risk behavior on Twitter with self-taught deep learning. Computational Social Networks 2019;6(1)
  4. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439
  5. Mavragani A, Ochoa G, Tsagarakis KP. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270
  6. Miliano C, Margiani G, Fattore L, De Luca M. Sales and Advertising Channels of New Psychoactive Substances (NPS): Internet, Social Networks, and Smartphone Apps. Brain Sciences 2018;8(7):123
  7. Mackey T, Kalyanam J, Klugman J, Kuzmenko E, Gupta R. Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access. Journal of Medical Internet Research 2018;20(4):e10029