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

Early Results in Infodemiology and Infoveillance (79) Infodemiology and Infoveillance (1375) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1674) Substance Abuse (431) Mass Media/Social Media Communication and Campaigns (470) Big Data (247) Development and Evaluation of Research Methods, Instruments and Tools (521) Opioid and Related Substance Misuse (150) Harm Reduction Approaches (14) Innovative Methods in Public Health and Surveillance (338) Public Health Informatics (95) Machine Learning (1517)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49567, first published .
An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

An Analysis of the Prevalence and Trends in Drug-Related Lyrics on Twitter (X): Quantitative Approach

Authors of this article:

Waylon Luo1 Author Orcid Image ;   Ruoming Jin1 Author Orcid Image ;   Deric Kenne2 Author Orcid Image ;   NhatHai Phan3 Author Orcid Image ;   Tang Tang4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Waylon Luo   1 , BA, MSci ;   Ruoming Jin   1 , PhD ;   Deric Kenne   2 , PhD ;   NhatHai Phan   3 , PhD ;   Tang Tang   4 , PhD

1 Department of Computer Science, Kent State University, Kent, OH, United States

2 College of Public Health, Kent State University, Kent, OH, United States

3 Department of Data Science, New Jersey Institute of Technology, Newark, NJ, United States

4 School of Media and Journalism, Kent State University, Kent, OH, United States

Corresponding Author:

  • Ruoming Jin, PhD
  • Department of Computer Science
  • Kent State University
  • 1300 Lester A Lefton Esplanade
  • Kent, OH, 44241
  • United States
  • Phone: 1 330-672-9063
  • Email: rjin1@kent.edu