{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:23:10Z","timestamp":1790792590271,"version":"4.1.0"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation Graduate Research Fellowship","doi-asserted-by":"publisher","award":["DGE-1656518"],"award-info":[{"award-number":["DGE-1656518"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>Drug abuse is a serious problem in the United States, with over 90,000 drug overdose deaths nationally in 2020. A key step in combating drug abuse is detecting, monitoring, and characterizing its trends over time and location, also known as pharmacovigilance. While federal reporting systems accomplish this to a degree, they often have high latency and incomplete coverage. Social-media-based pharmacovigilance has zero latency, is easily accessible and unfiltered, and benefits from drug users being willing to share their experiences online pseudo-anonymously. However, unlike highly structured official data sources, social media text is rife with misspellings and slang, making automated analysis difficult. Generative Pretrained Transformer 3 (GPT-3) is a large autoregressive language model specialized for few-shot learning that was trained on text from the entire internet. We demonstrate that GPT-3 can be used to generate slang and common misspellings of terms for drugs of abuse. We repeatedly queried GPT-3 for synonyms of drugs of abuse and filtered the generated terms using automated Google searches and cross-references to known drug names. When generated terms for alprazolam were manually labeled, we found that our method produced 269 synonyms for alprazolam, 221 of which were new discoveries not included in an existing drug lexicon for social media. We repeated this process for 98 drugs of abuse, of which 22 are widely-discussed drugs of abuse, building a lexicon of colloquial drug synonyms that can be used for pharmacovigilance on social media.<\/jats:p>","DOI":"10.3390\/biom13020387","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T03:56:07Z","timestamp":1676865367000},"page":"387","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4570-5170","authenticated-orcid":false,"given":"Kristy A.","family":"Carpenter","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3859-2905","authenticated-orcid":false,"given":"Russ B.","family":"Altman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA"},{"name":"Departments of Bioengineering, Genetics, and Medicine, Stanford University, Stanford, CA 94305, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1053\/j.semperi.2019.01.001","article-title":"The United States opioid epidemic","volume":"43","author":"Lyden","year":"2019","journal-title":"Semin. Perinatol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1097\/YCO.0000000000000717","article-title":"The Rise of Illicit Fentanyls, Stimulants and the Fourth Wave of the Opioid Overdose Crisis","volume":"34","author":"Ciccarone","year":"2021","journal-title":"Curr. Opin. Psychiatry"},{"key":"ref_3","unstructured":"CDC. National Center for Health Statistics (2022, December 19). 2021. Wide-Ranging Nnline Data for Epidemiologic Research (WONDER), Available online: http:\/\/wonder.cdc.gov."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1016\/j.clinthera.2018.07.012","article-title":"Pharmacovigilance: An Overview","volume":"40","author":"Beninger","year":"2018","journal-title":"Clin. Ther."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1056\/NEJMp1806486","article-title":"The FDA and the Next, Wave of Drug Abuse \u2014 Proactive Pharmacovigilance","volume":"379","author":"Throckmorton","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e1917228","DOI":"10.1001\/jamanetworkopen.2019.17228","article-title":"Trends in Prescription Analgesic Use Among Adults With Musculoskeletal Conditions in the United States, 1999\u20132016","volume":"2","author":"Stokes","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1002\/cncr.32059","article-title":"The role of cancer in marijuana and prescription opioid use in the United States: A population-based analysis from 2005 to 2014","volume":"125","author":"Tringale","year":"2019","journal-title":"Cancer"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.2147\/DHPS.S214771","article-title":"Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic","volume":"11","author":"Veronin","year":"2019","journal-title":"Drug Healthc. Patient Saf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"106846","DOI":"10.1016\/j.ypmed.2021.106846","article-title":"Prescription opioid use among women of reproductive age in the United States: NHANES, 2003\u20132018","volume":"153","author":"Elmore","year":"2021","journal-title":"Prev. Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Robert, M., Jouanjus, E., Khouri, C., Sam-La\u00ef, N.F., and Revol, B. (2022). The opioid epidemic: A worldwide exploratory study using the WHO pharmacovigilance database. Addiction.","DOI":"10.1111\/add.16081"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chiappini, S., Vickers-Smith, R., Guirguis, A., Corkery, J.M., Martinotti, G., Harris, D.R., and Schifano, F. (2022). Pharmacovigilance Signals of the Opioid Epidemic over 10 Years: Data Mining Methods in the Analysis of Pharmacovigilance Datasets Collecting Adverse Drug Reactions (ADRs) Reported to EudraVigilance (EV) and the FDA Adverse Event Reporting System (FAERS). Pharmaceuticals, 15.","DOI":"10.3390\/ph15060675"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1097\/EDE.0000000000001490","article-title":"Causal Effect of Chronic Pain on Mortality Through Opioid Prescriptions: Application of the Front-Door Formula","volume":"33","author":"Inoue","year":"2022","journal-title":"Epidemiology"},{"key":"ref_13","first-page":"100130","article-title":"A descriptive analysis of concomitant opioid and benzodiazepine medication use and associated adverse drug events in United States adults between 2009 and 2018","volume":"5","author":"Marwitz","year":"2022","journal-title":"Explor. Res. Clin. Soc. Pharm."},{"key":"ref_14","first-page":"109","article-title":"Pharmacovigilance using Clinical Text","volume":"2013","author":"LePendu","year":"2013","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1007\/s40264-014-0218-z","article-title":"Text mining for adverse drug events: The promise, challenges, and state of the art","volume":"37","author":"Harpaz","year":"2014","journal-title":"Drug Saf."},{"key":"ref_16","first-page":"196","article-title":"Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events","volume":"2015","author":"Boland","year":"2015","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1007\/s40264-017-0558-6","article-title":"Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review","volume":"40","author":"Luo","year":"2017","journal-title":"Drug Saf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ward, P.J., Rock, P.J., Slavova, S., Young, A.M., Bunn, T.L., and Kavuluru, R. (2019). Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0223318"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/aje\/kwac180","article-title":"Deep Neural Networks for Fine-Grained Surveillance of Overdose Mortality","volume":"192","author":"Ward","year":"2022","journal-title":"Am. J. Epidemiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1093\/pubmed\/fdx020","article-title":"Systematic review of surveillance by social media platforms for illicit drug use","volume":"39","author":"Kazemi","year":"2017","journal-title":"J. Public Health"},{"key":"ref_21","unstructured":"Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., and Gonzalez, G. (2010, January 15). Toward Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks. Proceedings of the of the 2010 Workshop on Biomedical Natural Language Processing, Uppsala, Sweden."},{"key":"ref_22","first-page":"924","article-title":"Pharmacovigilance on Twitter? Mining Tweets for Adverse Drug Reactions","volume":"2014","author":"Pimpalkhute","year":"2014","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Eshleman, R., and Singh, R. (2016). Leveraging graph topology and semantic context for pharmacovigilance through Twitter-streams. BMC Bioinform., 17.","DOI":"10.1186\/s12859-016-1220-5"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1093\/jamia\/ocw180","article-title":"Deep learning for pharmacovigilance: Recurrent neural network architectures for labeling adverse drug reactions in Twitter posts","volume":"24","author":"Cocos","year":"2017","journal-title":"J. Am. Med Inform. Assoc. JAMIA"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1080\/14740338.2018.1499724","article-title":"Evaluating Twitter as a complementary data source for pharmacovigilance","volume":"17","author":"Lardon","year":"2018","journal-title":"Expert Opin. Drug Saf."},{"key":"ref_26","first-page":"442","article-title":"Leveraging digital media data for pharmacovigilance","volume":"2020","author":"Farooq","year":"2020","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.1093\/jamia\/ocab114","article-title":"DeepADEMiner: A deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter","volume":"28","author":"Magge","year":"2021","journal-title":"J. Am. Med Inform. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s40264-016-0491-0","article-title":"Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts","volume":"40","author":"Pierce","year":"2017","journal-title":"Drug Saf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1007\/s40264-018-0699-2","article-title":"Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project","volume":"41","author":"Caster","year":"2018","journal-title":"Drug Saf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e32543","DOI":"10.2196\/32543","article-title":"Artificial Intelligence\u2013Enabled Social Media Analysis for Pharmacovigilance of COVID-19 Vaccinations in the United Kingdom: Observational Study","volume":"8","author":"Hussain","year":"2022","journal-title":"JMIR Public Health Surveill."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1002\/pds.5070","article-title":"Memantine misuse and social networks: A content analysis of Internet self-reports","volume":"29","author":"Natter","year":"2020","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e27714","DOI":"10.2196\/27714","article-title":"Quantifying the Severity of Adverse Drug Reactions Using Social Media: Network Analysis","volume":"23","author":"Lavertu","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e33919","DOI":"10.2196\/33919","article-title":"Using Named Entity Recognition to Identify Substances Used in the Self-medication of Opioid Withdrawal: Natural Language Processing Study of Reddit Data","volume":"6","author":"Preiss","year":"2022","journal-title":"JMIR Form. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"171","DOI":"10.3961\/jpmph.20.094","article-title":"Impact of Rumors and Misinformation on COVID-19 in Social Media","volume":"53","author":"Tasnim","year":"2020","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e17187","DOI":"10.2196\/17187","article-title":"Prevalence of Health Misinformation on Social Media: Systematic Review","volume":"23","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1080\/14740338.2018.1531847","article-title":"The usefulness of listening social media for pharmacovigilance purposes: A systematic review","volume":"17","author":"Convertino","year":"2018","journal-title":"Expert Opin. Drug Saf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tricco, A.C., Zarin, W., Lillie, E., Jeblee, S., Warren, R., Khan, P.A., Robson, R., Pham, B., Hirst, G., and Straus, S.E. (2018). Utility of social media and crowd-intelligence data for pharmacovigilance: A scoping review. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0621-y"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1007\/s41060-019-00175-3","article-title":"Harnessing social media data for pharmacovigilance: A review of current state of the art, challenges and future directions","volume":"8","author":"Pappa","year":"2019","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e30137","DOI":"10.2196\/30137","article-title":"The Use of Social Media in Detecting Drug Safety\u2013Related New Black Box Warnings, Labeling Changes, or Withdrawals: Scoping Review","volume":"7","author":"Lee","year":"2021","journal-title":"JMIR Public Health Surveill."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103307","DOI":"10.1016\/j.jbi.2019.103307","article-title":"RedMed: Extending drug lexicons for social media applications","volume":"99","author":"Lavertu","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_41","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: A major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"NUcleic Acids Res."},{"key":"ref_43","unstructured":"Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., and Askell, A. (2020, January 6\u201312). Language Models are Few-Shot Learners. Proceedings of the NeurIPS 2020, Virtual."},{"key":"ref_44","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., \u0141ukasz, K., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the NeurIPS 2017, Long Beach, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1038\/s41746-021-00464-x","article-title":"Considering the possibilities and pitfalls of Generative Pre-trained Transformer 3 (GPT-3) in healthcare delivery","volume":"4","author":"Korngiebel","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e32875","DOI":"10.2196\/32875","article-title":"Operationalizing and Implementing Pretrained, Large Artificial Intelligence Linguistic Models in the US Health Care System: Outlook of Generative Pretrained Transformer 3 (GPT-3) as a Service Model","volume":"10","author":"Sezgin","year":"2022","journal-title":"JMIR Med. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1136\/bjophthalmol-2022-321141","article-title":"New meaning for NLP: The trials and tribulations of natural language processing with GPT-3 in ophthalmology","volume":"106","author":"Nath","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"ref_48","unstructured":"Mcguffie, K., and Newhouse, A. (2020). The radicalization risks of GPT-3 and advanced neural language models. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Abid, A., Farooqi, M., and Zou, J. (2021). Persistent Anti-Muslim Bias in Large Language Models. arXiv.","DOI":"10.1145\/3461702.3462624"},{"key":"ref_50","unstructured":"(2022, July 25). U.S. Drug Enforcement Administration, Controlled Substances\u2014Alphabetical Order, Available online: https:\/\/www.deadiversion.usdoj.gov\/schedules\/orangebook\/c_cs_alpha.pdf."},{"key":"ref_51","first-page":"1362","article-title":"Tracking Health Related Discussions on Reddit for Public Health Applications","volume":"2017","author":"Park","year":"2017","journal-title":"AMIA Annu. Symp. Proc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1177\/0022042619833911","article-title":"Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List","volume":"49","author":"Adams","year":"2019","journal-title":"J. Drug Issues"},{"key":"ref_53","unstructured":"OpenAI (2022, December 16). ChatGPT: Optimizing Language Models for Dialogue. Available online: https:\/\/openai.com\/blog\/chatgpt\/."}],"container-title":["Biomolecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-273X\/13\/2\/387\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:35:51Z","timestamp":1760121351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-273X\/13\/2\/387"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,18]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["biom13020387"],"URL":"https:\/\/doi.org\/10.3390\/biom13020387","relation":{},"ISSN":["2218-273X"],"issn-type":[{"value":"2218-273X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,18]]}}}