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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jun 2, 2023
Open Peer Review Period: Jun 2, 2023 - Jun 16, 2023
Date Accepted: Oct 21, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Luo W, Jin R, Kenne D, Phan N, Tang T

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

JMIR Form Res 2024;8:e49567

DOI: 10.2196/49567

PMID: 39753225

PMCID: 11729777

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

  • Waylon Luo; 
  • Ruoming Jin; 
  • Deric Kenne; 
  • NhatHai Phan; 
  • Tang Tang

ABSTRACT

Background:

The pervasiveness of drug culture has become evident in popular music and social media. Previous research has examined drug abuse content in both social media and popular music; however, to our knowledge, the intersection of drug abuse content in these two domains has not been explored. To address the ongoing drug epidemic, we conducted an analysis of drug-related content on Twitter (now ‘X’), with a specific focus on lyrics. Our study provides a unique perspective on the prevalence of drug abuse in the United States.

Objective:

We aim to investigate drug trends in popular music on Twitter, identify and classify popular drugs, and analyze related artists' gender, genre, and popularity. Based on the collected data, our goal is to create a prediction model for future drug trends and gain a deeper understanding of the characteristics of users who cite drug lyrics on Twitter.

Methods:

Twitter data were collected from 2015 to 2017 via the Twitter streaming API (application programming interface). Drug-related lyrics were obtained from the Genius lyrics database by filtering using the Genius API based on drug keywords. Quantitative analysis methods were applied to identify famous drugs in lyrics being tweeted. Then the analysis was extended to related artists, songs, genres, and popularity. We leveraged time series analysis to create a prediction model for drug lyric trends on Twitter. A histogram distribution method was used to study one of the user characteristics: the number of followers of Twitter users who tweeted drug lyrics.

Results:

Over 1.97 billion publicly available tweets from 2015 to 2017 were analyzed, resulting in the identification of over 157 million tweets that matched drug keywords. Among them, 150,746 tweets were identified as referencing drug lyrics, revealing a decline in the number of drug lyrics over the three-year period, contradicting our initial hypothesis. Cannabinoids, opioids, stimulants, and hallucinogens emerged as the most frequently mentioned drugs in lyrics. The majority of drug-related lyrics on Twitter (91.98%) belonged to the Rap or Hip-Hop genres, with male artists accounting for 84.21% of the performances.

Conclusions:

Our analysis of drug lyrics posted on Twitter provides novel insights that could serve as an indicator of the ongoing drug epidemic.


 Citation

Please cite as:

Luo W, Jin R, Kenne D, Phan N, Tang T

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

JMIR Form Res 2024;8:e49567

DOI: 10.2196/49567

PMID: 39753225

PMCID: 11729777

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