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

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1330) Artificial Intelligence (665) Cyberpharmacies (31) Infodemiology and Infoveillance (1005) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1273) Big Data (169) Machine Learning (1004) Natural Language Processing (490) Infectious Diseases (non-STD/STI) (1224) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (822) Mass Media/Social Media Communication and Campaigns (401) Outbreak and Pandemic Preparedness and Management (1454) Infoveillance and Social Listening (134)

Published on in Vol 6 , No 3 (2020) :Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20794, first published .
Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram

Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram

Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: Infoveillance Study on Twitter and Instagram

Authors of this article:

Tim Ken Mackey 1, 2, 3, 4 Author Orcid Image ;   Jiawei Li 2, 3, 4 Author Orcid Image ;   Vidya Purushothaman 4, 5 Author Orcid Image ;   Matthew Nali 1, 3, 4 Author Orcid Image ;   Neal Shah 2, 4 Author Orcid Image ;   Cortni Bardier 6 Author Orcid Image ;   Mingxiang Cai 3, 4 Author Orcid Image ;   Bryan Liang 4 Author Orcid Image
Article Authors Cited by (45) Tweetations (89) Metrics

Journals

  1. Mackey T, Purushothaman V, Haupt M, Nali M, Li J. Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter. The Lancet Digital Health 2021;3(2):e72 View
  2. Miró-Llinares F. Crimen, cibercrimen y COVID-19: desplazamiento (acelerado) de oportunidades y adaptación situacional de ciberdelitos. IDP. Revista de Internet Derecho y Política 2021;(32) View
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  4. Cuomo R, Purushothaman V, Li J, Cai M, Mackey T. A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States. BMC Public Health 2021;21(1) View
  5. Piccialli F, di Cola V, Giampaolo F, Cuomo S. The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic. Information Systems Frontiers 2021;23(6):1467 View
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  7. Braun S, O’Connor M, Hornick M, Cullom M, Butterworth J. Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts. Aesthetic Surgery Journal 2021;41(11):1323 View
  8. Rovetta A. Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research. Frontiers in Research Metrics and Analytics 2021;6 View
  9. Riswantini D, Nugraheni E, Arisal A, Khotimah P, Munandar D, Suwarningsih W. Big Data Research in Fighting COVID-19: Contributions and Techniques. Big Data and Cognitive Computing 2021;5(3):30 View
  10. Fittler A, Adeniye L, Katz Z, Bella R. Effect of Infodemic Regarding the Illegal Sale of Medications on the Internet: Evaluation of Demand and Online Availability of Ivermectin during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(14):7475 View
  11. Pian W, Chi J, Ma F. The causes, impacts and countermeasures of COVID-19 “Infodemic”: A systematic review using narrative synthesis. Information Processing & Management 2021;58(6):102713 View
  12. Abdeen M, Hamed A, Wu X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Applied Sciences 2021;11(16):7265 View
  13. Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850 View
  14. Haupt M, Cuomo R, Li J, Nali M, Mackey T. The influence of social media affordances on drug dealer posting behavior across multiple social networking sites (SNS). Computers in Human Behavior Reports 2022;8:100235 View
  15. Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearbook of Medical Informatics 2021;30(01):257 View
  16. Del Vecchio P, Mele G, Siachou E, Schito G. A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing. International Marketing Review 2022;39(5):1069 View
  17. Taodang D, Gundur R. How Frauds in Times of Crisis Target People. Victims & Offenders 2022:1 View
  18. Fatima R, Samad Shaikh N, Riaz A, Ahmad S, El-Affendi M, Alyamani K, Nabeel M, Ali Khan J, Yasin A, Latif R, Javed A. A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques. Computational Intelligence and Neuroscience 2022;2022:1 View
  19. Fuad M, Suyanto E, Sumarno , Muhammad U, Suparman . A Bibliometric Analysis of Technology-Based Foreign Language Learning during the COVID-19 Pandemic: Direction for Indonesia Language Learning. International Journal of Information and Education Technology 2022;12(10):983 View
  20. Gunasekeran D, Chew A, Chandrasekar E, Rajendram P, Kandarpa V, Rajendram M, Chia A, Smith H, Leong C. The Impact and Applications of Social Media Platforms for Public Health Responses Before and During the COVID-19 Pandemic: Systematic Literature Review. Journal of Medical Internet Research 2022;24(4):e33680 View
  21. Alhabash S, Dong Y, Moureaud C, Muraro I, Hertig J. Effects of Fear and Humor Appeals in Public Service Announcements (PSAs) on Intentions to Purchase Medications via Social Media. International Journal of Environmental Research and Public Health 2022;19(19):12340 View
  22. Zarei K, Farahbakhsh R, Crespi N, Tyson G. Dataset of Coronavirus Content From Instagram With an Exploratory Analysis. IEEE Access 2021;9:157192 View
  23. Chicaiza J, Villota S, Vinueza-Naranjo P, Rumipamba-Zambrano R. Contribution of Deep-Learning Techniques Toward Fighting COVID-19: A Bibliometric Analysis of Scholarly Production During 2020. IEEE Access 2022;10:33281 View
  24. Mackey T, Jarmusch A, Xu Q, Sun K, Lu A, Aguirre S, Lim J, Bhakta S, Dorrestein P. Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study. JMIR Public Health and Surveillance 2022;8(12):e41834 View
  25. Caskey J, McConnell I, Oguss M, Dligach D, Kulikoff R, Grogan B, Gibson C, Wimmer E, DeSalvo T, Nyakoe-Nyasani E, Churpek M, Afshar M. Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline. JMIR Public Health and Surveillance 2022;8(3):e36119 View
  26. Li F, Wang L. Research on Talent Training Mode of Computer Undergraduate Major in Finance and Economics Colleges. WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 2022;19:121 View
  27. Beliga S, Martinčić-Ipšić S, Matešić M, Petrijevčanin Vuksanović I, Meštrović A. Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing. JMIR Public Health and Surveillance 2021;7(12):e31540 View
  28. Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. International Journal of Applied Earth Observation and Geoinformation 2022;113:102967 View
  29. Turner J, Kantardzic M, Vickers-Smith R, Brown A. Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification. JMIR Infodemiology 2023;3:e38390 View
  30. Ng J, Abdelkader W, Lokker C. Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data. BMC Complementary Medicine and Therapies 2022;22(1) View
  31. Mbaya E, Alao B, Ewejobi P, Nwokolo I, Oguntosin V, Adetiba E. NaijaCovidAPI: an application programming interface for retrieval of COVID19 data from the Nigerian Center for Disease Control web platform. F1000Research 2021;10:1227 View
  32. Johnson S, Nikolovska M. The Effect of COVID-19 Restrictions on Routine Activities and Online Crime. Journal of Quantitative Criminology 2022 View
  33. Kasson E, Singh A, Huang M, Wu D, Cavazos-Rehg P. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak. International Journal of Medical Informatics 2021;155:104574 View
  34. Soon-Sinclair J, Nyarugwe S, Jack L. Food fraud and mitigating strategies of UK food supply chain during COVID-19. Food Control 2023;148:109670 View
  35. Honcharov V, Li J, Sierra M, Rivadeneira N, Olazo K, Nguyen T, Mackey T, Sarkar U. Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis. JMIR Infodemiology 2023;3:e40575 View
  36. Quinn E, Fenton S, Ford-Sahibzada C, Harper A, Marcon A, Caulfield T, Fazel S, Peters C. COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis. JMIR Infodemiology 2022;2(1):e32452 View
  37. Chang H, Tsai F. A Systematic Review of Internet Public Opinion Manipulation. Procedia Computer Science 2022;207:3159 View
  38. Assi S, Abbas I, Arafat B, Evans K, Al-Jumeily D. Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy. Journal of Fluorescence 2023 View
  39. Sarker A, Lakamana S, Liao R, Abbas A, Yang Y, Al-Garadi M. The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection. JMIR Infodemiology 2023;3:e43694 View
  40. Diareme K, Liapakis A, Efthymiou I. Big Data, Sentiment Analysis, and Examples during the COVID-19 Pandemic. HAPSc Policy Briefs Series 2022;3(2):21 View

Books/Policy Documents

  1. Gundur R, Berry M, Taodang D. Researching Cybercrimes. View
  2. Heyerdahl L, Lana B, Giles-Vernick T. Socio-Life Science and the COVID-19 Outbreak. View