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

Infoveillance and Social Listening (309) JMIR Theme Issue: COVID-19 Special Issue (2546) Natural Language Processing (896) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1899) Substance Abuse (522) Demographics of Users, Social & Digital Divide (902) Public Health Policy (88) Artificial Intelligence (2068) Information Demand (44)

Published on in Vol 5 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67333, first published .
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

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

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

Authors of this article:

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

Journals

  1. Maharjan J, Jin R, Zhu J, Kenne D. Intersection of Big Five Personality Traits and Substance Use on Social Media Discourse: An AI-Powered Cross-Sectional Study with Temporal Comparisons (Preprint). Journal of Medical Internet Research 2025 View