Detecting novel and emerging drug terms using natural language processing:a social media corpus study

JMIR Public Health and Surveillance

Volumn 4, Issue 1, 2018, Pages

  Simpson, Sean S ^a   Adams, Nikki ^b   Brugman, Claudia M ^b   Conners, Thomas J ^b  

^a GEORGETOWN UNIVERSITY   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

Natural language processing; Social media; Street drugs; Vocabulary

Indexed keywords

EID: 85041058084     PISSN: None     EISSN: 23692960     Source Type: Journal    
DOI: 10.2196/publichealth.7726     Document Type: Article

References (34)

1. Allan K, Burridge K. Forbidden words: Taboo and the censoring of language. Cambridge: Cambridge University Press; 2006.
2. Luginböhl M. Media linguistics: On mediality and culturality. 10plus1 Living Linguistics 2015 Sep 15;1:9-26
3. Paul M, Dredze M. You Are What You Tweet: Analyzing Twitter for Public Health. 2011 Presented at: Fifth International AAAI Conference on Weblogs and Social Media; 2011; Barcelona, San Francisco p. 265-272 URL: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewFile/2880/3264
4. Tricco AC, Zarin W, Lillie E, Pham B, Straus SE. Utility of social media and crowd-sourced data for pharmacovigilance: A scoping review protocol. BMJ Open 2017 Jan 19;7(1):e013474 [doi: 10.1136/bmjopen-2016-013474]
5. Sinnenberg L, Buttenheim AM, Padrez K, Mancheno C, Ungar L, Merchant RM. Twitter as a tool for health research: A systematic review. Am J Public Health 2017 Jan;107(1):e1-e8. [doi: 10.2105/AJPH.2016.303512]
6. Hanson C, Cannon B, Burton S, Ciraud-Carrier C. An exploration of social circles and prescription drug abuse through Twitter. J. Med.Internet.Res (189) 2013 Sep 06;2013(9):15. [doi: 10.2196/jmir.2741]
7. May T, Hough M. Drug markets and distribution systems. Addict Res Theory 2009;12(6):549-563. [doi: 10.1080/16066350412331323119]
8. May T, Duffy M, Few B, Hough M. Understanding Drug Selling in Communities: Insider or Outsider Trading?. York, UK: Joseph Rowntree Foundation; 2006.
9. Ritter A. Studying illicit drug markets: disciplinary contributions. Int J Drug Policy 2006 Dec;17(6):453-463. [doi: 10.1016/j.drugpo.2006.09.004]
10. Hanson C, Burton S, Giraud-Carrier C, West JH, Barnes MD, Hansen B. Tweaking and tweeting: exploring Twitter for nonmedical use of a psychostimulant drug (Adderall) among college students. J Med Internet Res 2013 Apr 17;15(4):e62 [doi: 10.2196/jmir.2503]
11. Harris ZS. Distributional structure. Word 1954;10(2-3):146-162. [doi: 10.1080/00437956.1954.11659520]
12. Philological Society (Great Britain). A synopsis of linguistic theory, 1930-1955. In: Studies in Linguistic Analysis. Oxford: Blackwell; 1957:1-32.
13. Köffner R, Fundel K, Zimmer R. Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts. Bioinformatics 2005 Sep 01;21(Suppl 2):259-267. [doi: 10.1093/bioinformatics/bti1143]
14. Henriksson A, Moen H, Skeppstedt M, Eklund A, Daudaravicius V, Hassel M. Synonym extraction of medical terms from clinical text using combinations of word space models. 2012 Presented at: Proceedings from the 5th International Symposium on Semantic Mining in Biomedicine; 2012; Zurich p. 10-17 URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10. 1.1.298.2578&rep=rep1&type=pdf
15. Kanerva P, Kristoferson J, Holst A. Random Indexing of Text Samples for Latent Semantic Analysis. 2000 Presented at: Proceedings of the Annual Meeting of the Cognitive Science Society, 22; 2000; Philadelphia URL: http://escholarship.org/uc/item/5644k0w6
16. Sahlgren M, Holst A, Kanerva P. Permutations as a means to encode order in word space. 2008 Presented at: Conference Proceedings of the 30th Annual Cognitive Science Conference; 2008; Washington p. 1300-1305.
17. Henriksson A, Moen H, Skeppstedt M, Vidas D, Duneld M. Synonym extraction and abbreviation expansion with ensembles of semantic spaces. J Biomed Semantics 2014;5(6):1-25. [doi: 10.1186/2041-1480-5-6]
18. Bird S, Klein E, Loper E. Natural language processing with Python. 1st ed. Beijing: O'Reilly Media; 2009.
19. Malik M, Lamba H, Nakos C, Pfeffer J. Population bias in geotagged tweets. Standards and Practices in Large-Scale Social Media Research: Papers from the 2015 ICWSM Workshop 2015:18-27.
20. Substance Abuse and Mental Health Services Administration. SAMHSA. 2014. Results from the 2013 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-48, HHS Publication No. (SMA) 14-4863 URL: https://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDUHresults2013.pdf [accessed 2017-09-13] [WebCite Cache ID 6tRJKAdEw]
21. Bottorff JL, Bissell LJ, Balneaves J, Oliffe JL, Capler NR, Buxton J. Perceptions of cannabis as a stigmatized medicine: A qualitative descriptive study. Harm Reduct J 2013;10(1):2. [doi: 10.1186/1477-7517-10-2]
22. Johnston L, O'Malley P, Bachman J, Schulenberg E. Monitoringthefuture. 2010. Monitoring the Future: National survey results on drug use, 1975-2009 URL: http://monitoringthefuture.org/pubs/monographs/vol2-2009.pdf [accessed 2017-08-25] [WebCite Cache ID 6szIP3s33]
23. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed Representations of Words and Phrases and their Compositionality. In: Burges CJC, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, editors. Advances in Neural Information Processing Systems 26. Red Hook, NY: Curran Associates, Inc; 2013:3111-3119.
24. Rehurek R, Sojka P. Radimrehurek.: NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic; 2011. Gensim-Python Framework for Vector Space Mo delling URL: https://radimrehurek.com/gensim/[accessed 2017-09-13] [WebCite Cache ID 6tRJrNgH3]
25. Mikolov T, Chen K, Corrado G, Dean J. arxiv.org. 2013. Efficient estimation of word representations in vector space URL: https://arxiv.org/abs/1301.3781 [accessed 2017-08-25] [WebCite Cache ID 6szK9JeDW]
26. National Drug Early Warning System. Sentinel Sites URL: https://ndews.umd.edu/landing/Sentinel%20Sites [accessed 2017-09-13] [WebCite Cache ID 6tRGFiU0V]
27. DEA. Drug Fact Sheet: Marijuana URL: https://www.dea.gov/druginfo/drug-data-sheets/Marijuana.pdf [accessed 2017-02-06] [WebCite Cache ID 6o4v8ckMG]
28. CESAR. Marijuana URL: http://www.cesar.umd.edu/cesar/drugs/marijuana.asp [accessed 2017-02-01] [WebCite Cache ID 6nxZnYDfH]
29. Urban Dictionary. 2017. URL: http://www.urbandictionary.com [accessed 2017-02-01] [WebCite Cache ID 6nxa4NEwN]
30. Marijuana Dictionary. 2017. URL: https://web.archive.org/web/20161023112809/http://www.marijuanadictionary.com/[accessed 2017-02-06] [WebCite Cache ID 6o4v4EbzB]
31. Piergallini M, Dogruöz A, Gadde P, Adamson D, Rosé CP. Modeling the use of graffiti style features to signal social relations within a multi-domain learning paradigm. In: 14th Conference of the European Chapter of the Association for Computational Linguistics 2014 (EACL 2014). 2014 Presented at: EALC 2014; 26-30 April, 2014; Gothenburg p. 107-115 URL: http://tangra.cs.yale.edu/newaan/index.php/paper/view?paper-id=E14-1012
32. Mary Jane's Diary. Marijuana Emojis for Stealth Weed Texts URL: http://maryjanesdiary.com/10-marijuana-emojis-weed-Texts/[accessed 2017-06-13] [WebCite Cache ID 6rCDYsSW5]
33. Trask A, Michalak P, Liu J. Arxiv. 2015. Sense2vec-A fast and accurate method for word sense disambiguation in neural word embeddings URL: https://arxiv.org/abs/1511.06388 [accessed 2017-08-25] [WebCite Cache ID 6szKVDI65]
34. Anthiwaratkun A, Wilson AG. Arxiv. 2017. Multimodal word distributions URL: https://arxiv.org/abs/1704.08424 [accessed 2017-08-25] [WebCite Cache ID 6szKidwGc]