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

Journals

1. Lavertu A, Altman R. RedMed: Extending drug lexicons for social media applications. Journal of Biomedical Informatics 2019;99:103307 View
2. Adams N, Artigiani E, Wish E. Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List. Journal of Drug Issues 2019;49(3):477 View
3. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
4. 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 View
5. Hu H, Phan N, Chun S, 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) View
6. 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 View
7. Artigiani E, Wish E. Introducing the National Drug Early Warning System. Current Opinion in Psychiatry 2020;33(4):319 View
8. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
9. Li Z, Du X, Liao X, Jiang X, Champagne-Langabeer T. Demystifying the Dark Web Opioid Trade: Content Analysis on Anonymous Market Listings and Forum Posts. Journal of Medical Internet Research 2021;23(2):e24486 View
10. Xie J, Zhang Z, Liu X, Zeng D. Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning. Journal of Management Information Systems 2021;38(1):166 View
11. Wiesinger H, Wang Z, Hellweg S. Deep Dive into Plastic Monomers, Additives, and Processing Aids. Environmental Science & Technology 2021;55(13):9339 View
12. Saran S, Salinas K, Foulds J, Kaynak Ö, Hoglen B, Houser K, Krebs N, Yingst J, Allen S, Bordner C, Hobkirk A. A Comparison of Vaping Behavior, Perceptions, and Dependence among Individuals Who Vape Nicotine, Cannabis, or Both. International Journal of Environmental Research and Public Health 2022;19(16):10392 View
13. Fuller A, Vasek M, Mariconti E, Johnson S. Understanding and preventing the advertisement and sale of illicit drugs to young people through social media: A multidisciplinary scoping review. Drug and Alcohol Review 2024;43(1):56 View
14. Tang L, Korona-Bailey J, Zaras D, Roberts A, Mukhopadhyay S, Espy S, Walsh C. Using Natural Language Processing to Predict Fatal Drug Overdose From Autopsy Narrative Text: Algorithm Development and Validation Study. JMIR Public Health and Surveillance 2023;9:e45246 View
15. Yuan Y, Kasson E, Taylor J, Cavazos-Rehg P, De Choudhury M, Aledavood T. Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach. JMIR Formative Research 2024;8:e54433 View
16. Almeida A, Patton T, Conway M, Gupta A, Strathdee S, Bórquez A. The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review. JMIR Infodemiology 2024;4:e51156 View

Books/Policy Documents

1. González S, Sakata T, Nogueira R. Artificial Intelligence and Soft Computing. View
2. Lobantsev A, Loginova V, Burlakova Y, Andreev N, Matveeva V, Filimonova I, Dobrenko N, Gusarova N. Digital Transformation and Global Society. View
3. Vyas P, Vyas G, Chauhan A, Rawat R, Telang S, Gottumukkala M. Using Computational Intelligence for the Dark Web and Illicit Behavior Detection. View