Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30753, first published .
Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Journals

  1. Alibudbud R, Cleofas J. Global utilization of online information for substance use disorder: An infodemiological study of Google and Wikipedia from 2004 to 2022. Journal of Nursing Scholarship 2023;55(3):665 View
  2. Garcia G, Dehghanpoor R, Stringfellow E, Gupta M, Rochelle J, Mason E, Pujol T, Jalali M. Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use. International Journal of Environmental Research and Public Health 2022;19(10):6281 View
  3. ElSherief M, Sumner S, Krishnasamy V, Jones C, Law R, Kacha-Ochana A, Schieber L, De Choudhury M. Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study. JMIR Formative Research 2024;8:e44726 View
  4. Chi Y, Chen H. Investigating Substance Use via Reddit: Systematic Scoping Review. Journal of Medical Internet Research 2023;25:e48905 View
  5. 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

Books/Policy Documents

  1. Pendyala V. Deep Learning Research Applications for Natural Language Processing. View