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

Infoveillance and Social Listening (277) Infodemiology and Infoveillance (1497) Substance Abuse (502) Applications of AI (213) Research Methodology - AI (23)

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

Authors of this article:

Julina Maharjan1 Author Orcid Image ;   Jianfeng Zhu1 Author Orcid Image ;   Jennifer King2 Author Orcid Image ;   NhatHai Phan3 Author Orcid Image ;   Deric Kenne2 Author Orcid Image ;   Ruoming Jin1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

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