{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T23:22:23Z","timestamp":1771543343146,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The opioid drug overdose death rate remains a significant public health crisis in the U.S., where an opioid epidemic has led to a dramatic rise in overdose deaths over the past two decades. Since 1999, opioids have been implicated in approximately 75% of the nearly one million drug-related deaths. Research indicates that the epidemic is caused by both over-prescribing and social and psychological determinants such as economic stability, hopelessness, and social isolation. Impeding this research is the lack of measurements of these social and psychological constructs at fine-grained spatial and temporal resolution. To address this issue, we sourced data from Reddit, where people share self-reported experiences with opioid substances, specifically using opioid drugs through different routes of administration. To achieve this objective, an opioid overdose dataset is created and manually annotated in binary and multi-classification, along with detailed annotation guidelines. In traditional manual investigations, the route of administration is determined solely through biological laboratory testing. This study investigates the efficacy of an automated tool leveraging natural language processing and transformer model, such as RoBERTa, to analyze patterns of substance use. By systematically examining these patterns, the model contributes to public health surveillance efforts, facilitating the identification of at-risk populations and informing the development of targeted interventions. This approach ultimately aims to enhance prevention and treatment strategies for opioid misuse through data-driven insights. The findings show that our proposed methodology achieved the highest cross-validation score of 93% for binary classification and 91% for multi-class classification, demonstrating performance improvements of 9.41% and 10.98%, respectively, over the baseline model (XGB, 85% in binary class and 81% in multi-class).<\/jats:p>","DOI":"10.3390\/info16070545","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T03:33:25Z","timestamp":1750995205000},"page":"545","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Opioid Crisis Detection in Social Media Discourse Using Deep Learning Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8799-8212","authenticated-orcid":false,"given":"Muhammad","family":"Ahmad","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional (CIC-PN), Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3901-3522","authenticated-orcid":false,"given":"Grigori","family":"Sidorov","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional (CIC-PN), Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maaz","family":"Amjad","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1134-9713","authenticated-orcid":false,"given":"Iqra","family":"Ameer","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Pennsylvania State University at Abington, Abington, PA 19001, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ildar","family":"Batyrshin","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional (CIC-PN), Mexico City 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1056\/NEJMra1508490","article-title":"Relationship between nonmedical prescription-opioid use and heroin use","volume":"374","author":"Compton","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_2","first-page":"1419","article-title":"Drug and opioid-involved overdose deaths\u2014United States, 2013\u20132017","volume":"67","author":"Scholl","year":"2019","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_3","unstructured":"National Center for Health Statistics (2015, September 22). National Vital Statistics System, Available online: https:\/\/www.cdc.gov\/nchs\/nvss\/deaths.htm."},{"key":"ref_4","unstructured":"Substance Abuse and Mental Health Services Administration (SAMHSA) (2024, November 06). 2017 National Survey on Drug Use and Health: Detailed Tables, Available online: https:\/\/www.samhsa.gov\/data."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1207\/s15327752jpa6503_16","article-title":"Sexual sensation seeking and sexual compulsivity scales: Validity, and predicting HIV risk behavior","volume":"65","author":"Kalichman","year":"1995","journal-title":"J. Personal. Assess."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/S0899-3289(01)00069-4","article-title":"Substance-abusing adolescents at varying levels of HIV risk: Psychosocial characteristics, drug use, and sexual behavior","volume":"13","author":"Malow","year":"2001","journal-title":"J. Subst. Abus."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1111\/j.1360-0443.2010.02992.x","article-title":"Using the internet to research hidden populations of illicit drug users: A review","volume":"105","author":"Miller","year":"2010","journal-title":"Addiction"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e54433","DOI":"10.2196\/54433","article-title":"Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach","volume":"8","author":"Yuan","year":"2024","journal-title":"JMIR Form. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"eaau1184","DOI":"10.1126\/science.aau1184","article-title":"Changing dynamics of the drug overdose epidemic in the United States from 1979 through 2016","volume":"361","author":"Jalal","year":"2018","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"290","DOI":"10.15585\/mmwr.mm6911a4","article-title":"Drug and opioid-involved overdose deaths\u2014United States, 2017\u20132018","volume":"69","author":"Wilson","year":"2020","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_11","unstructured":"Food and Drug Administration (2024, November 06). Duragesic Prescribing Information, Available online: https:\/\/www.accessdata.fda.gov\/drugsatfda_docs\/label\/2019\/019813s079lbl.pdf."},{"key":"ref_12","unstructured":"Food and Drug Administration (2024, November 06). Fentora Prescribing Information, Available online: https:\/\/www.accessdata.fda.gov\/drugsatfda_docs\/label\/2019\/021947s029lbl.pdf."},{"key":"ref_13","unstructured":"Food and Drug Administration (2024, November 06). Fentanyl Citrate Prescribing Information, Available online: https:\/\/www.accessdata.fda.gov\/drugsatfda_docs\/label\/2019\/019115s033lbl.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/1477-7517-8-29","article-title":"Abuse risks and routes of administration of different prescription opioid compounds and formulations","volume":"8","author":"Butler","year":"2011","journal-title":"Harm Reduct. J."},{"key":"ref_15","unstructured":"Spencer, M.R., Warner, M., Bastian, B.A., Trinidad, J.P., and Hedegaard, H. (2019). Drug Overdose Deaths Involving Fentanyl, 2011\u20132016."},{"key":"ref_16","unstructured":"Drug Enforcement Administration (2024, November 06). NFLIS Drug Brief: Fentanyl; U.S. Department of Justice, Available online: https:\/\/www.nflis.deadiversion.usdoj.gov\/nflisdata\/docs\/15431NFLISDrugBriefFentanyl.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"897","DOI":"10.15585\/mmwr.mm6634a2","article-title":"Trends in deaths involving heroin and synthetic opioids excluding methadone, and law enforcement drug product reports, by census region\u2014United States, 2006\u20132015","volume":"66","year":"2017","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e189","DOI":"10.2196\/jmir.2741","article-title":"An exploration of social circles and prescription drug abuse through Twitter","volume":"15","author":"Hanson","year":"2013","journal-title":"J. Med. Internet Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100846","DOI":"10.1016\/j.hlpt.2024.100846","article-title":"The social media Infodemic of health-related misinformation and technical solutions","volume":"13","author":"Rodrigues","year":"2024","journal-title":"Health Policy Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"De Choudhury, M., and De, S. (2014, January 1\u20134). Mental health discourse on reddit: Self-disclosure, social support, and anonymity. Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA.","DOI":"10.1609\/icwsm.v8i1.14526"},{"key":"ref_21","unstructured":"Enes, K.B., Brum, P.P.V., Cunha, T.O., Murai, F., da Silva, A.P.C., and Pappa, G.L. (2018, January 3\u20136). Reddit weight loss communities: Do they have what it takes for effective health interventions?. Proceedings of the 2018 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI), Santiago, Chile."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1145\/3361108","article-title":"The language of LGBTQ+ minority stress experiences on social media","volume":"3","author":"Saha","year":"2019","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_23","unstructured":"Lu, J., Sridhar, S., Pandey, R., Hasan, M.A., and Mohler, G. (2019). Redditors in recovery: Text mining reddit to investigate transitions into drug addiction. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chancellor, S., Nitzburg, G., Hu, A., Zampieri, F., and De Choudhury, M. (2019, January 4\u20139). Discovering alternative treatments for opioid use recovery using social media. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK.","DOI":"10.1145\/3290605.3300354"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.2105\/AJPH.2017.303994","article-title":"Twitter-based detection of illegal online sale of prescription opioid","volume":"107","author":"Mackey","year":"2017","journal-title":"Am. J. Public Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.addbeh.2016.08.019","article-title":"Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning","volume":"65","author":"Kalyanam","year":"2017","journal-title":"Addict. Behav."},{"key":"ref_27","unstructured":"Blackley, S.V., MacPhaul, E., Martin, B., Song, W., Suzuki, J., and Zhou, L. (November, January 30). Using natural language processing and machine learning to identify hospitalized patients with opioid use disorder. Proceedings of the AMIA Annual Symposium, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s13011-022-00442-w","article-title":"Concerns among people who use opioids during the COVID-19 pandemic: A natural language processing analysis of social media posts","volume":"17","author":"Sarker","year":"2022","journal-title":"Subst. Abus. Treat. Prev. Policy"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wright, A.P., Jones, C.M., Chau, D.H., Gladden, R.M., and Sumner, S.A. (2021). Detection of emerging drugs involved in overdose via diachronic word embeddings of substances discussed on social media. J. Biomed. Inform., 119.","DOI":"10.1016\/j.jbi.2021.103824"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1002\/pds.4772","article-title":"Identifying and classifying opioid-related overdoses: A validation study","volume":"28","author":"Green","year":"2019","journal-title":"Pharmacoepidemiol. Drug Saf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1093\/aje\/kwab279","article-title":"Identifying predictors of opioid overdose death at a neighborhood level with machine learning","volume":"191","author":"Schell","year":"2022","journal-title":"Am. J. Epidemiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1080\/15228835.2017.1416511","article-title":"Machine learning for drug overdose surveillance","volume":"36","author":"Neill","year":"2018","journal-title":"J. Technol. Hum. Serv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dong, X., Deng, J., Hou, W., Rashidian, S., Rosenthal, R.N., Saltz, M., and Wang, F. (2021). Predicting opioid overdose risk of patients with opioid prescriptions using electronic health records based on temporal deep learning. J. Biomed. Inform., 116.","DOI":"10.1016\/j.jbi.2021.103725"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e241342","DOI":"10.1001\/jamanetworkopen.2024.1342","article-title":"Older Adult and Primary Care Practitioner Perspectives on Using, Prescribing, and Deprescribing Opioids for Chronic Pain","volume":"7","author":"Anderson","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1097\/ALN.0000000000004863","article-title":"Incidence and Prevalence of Pain Medication Prescriptions in Pathologies with a Potential for Chronic Pain","volume":"140","author":"Goudman","year":"2024","journal-title":"Anesthesiology"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Graham, S.S., Shifflet, S., Amjad, M., and Claborn, K. (2024). An interpretable machine learning framework for opioid overdose surveillance from emergency medical services records. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0292170"},{"key":"ref_37","unstructured":"Abuse, S., and Mental Health Services Administration (2014). Results from the 2013 National Survey on Drug Use and Health: Mental Health Findings."},{"key":"ref_38","first-page":"1487","article-title":"Vital Signs: Overdoses of Prescription Opioid Pain Relievers--United States, 1999\u20132008","volume":"60","author":"Paulozzi","year":"2011","journal-title":"MMWR Morb. Mortal. Wkly. Rep."},{"key":"ref_39","first-page":"1","article-title":"Drug poisoning deaths in the United States, 1980\u20132008","volume":"81","author":"Warner","year":"2011","journal-title":"NCHS Data Brief"},{"key":"ref_40","first-page":"120","article-title":"Obesity\u2014United States, 1999\u20132010","volume":"62","author":"May","year":"2013","journal-title":"MMWR Surveill. Summ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1111\/ajt.13776","article-title":"Increases in drug and opioid overdose deaths\u2014United States, 2000\u20132014","volume":"16","author":"Rudd","year":"2016","journal-title":"Am. J. Transplant."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1108\/JD-01-2021-0022","article-title":"Health information-seeking behavior in the time of COVID-19: Information horizons methodology to decipher source path during a global pandemic","volume":"77","author":"Zimmerman","year":"2021","journal-title":"J. Doc."},{"key":"ref_43","unstructured":"Brown University (2024, November 06). Cold weather increases the risk of fatal opioid overdoses, study finds. News from Brown, Available online: https:\/\/www.brown.edu\/news\/2019-06-17\/cold-overdoses."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s11135-014-0003-1","article-title":"Fleiss\u2019 kappa statistic without paradoxes","volume":"49","author":"Falotico","year":"2015","journal-title":"Qual. Quant."},{"key":"ref_46","first-page":"2825","article-title":"Scikit-learn: Machine learning in python Fabian","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_47","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., and Zheng, X. (2016, January 2\u20134). {TensorFlow}: A system for {Large-Scale} machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_48","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/545\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:59:43Z","timestamp":1760032783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/545"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070545"],"URL":"https:\/\/doi.org\/10.3390\/info16070545","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]}}}