Published on in Vol 5 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/59076, first published .
Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic

Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic

Large-Scale Deep Learning–Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic

Journals

  1. Maharjan J, Jin R, King J, Zhu J, Kenne D. Differential Analysis of Age, Gender, Race, Sentiment, and Emotion in Substance Use Discourse on Twitter During the COVID-19 Pandemic: A Natural Language Processing Approach. JMIR Infodemiology 2025;5:e67333 View
  2. Maharjan J, Jin R, Zhu J, Kenne D. Intersection of Big Five Personality Traits and Substance Use on Social Media Discourse: AI-Powered Observational Study. Journal of Medical Internet Research 2025;27:e79454 View
  3. Sidorov G, Ahmad M, Basile P, Waqas M, Orji R, Batyrshin I. Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis. JMIR Infodemiology 2025;5:e77279 View
  4. . Benchmarking Personality Inference in Large Language Models Using Real-World Conversations. Journal of Psychiatry and Brain Science 2025;10(6) View