The Marketing and Perceptions of Non-Tobacco Blunt Wraps on Twitter

Abstract

Objective

Non-tobacco blunt wraps (N-TBWs), which entered the marketplace in 2017, are being promoted as an alternative to traditional TBWs (e.g., cigarillos) for blunt smoking. The lack of studies on these novel products warrants an investigation. This study was the first to explore blunt smokers’ perceptions about N-TBWs and the extent of product marketing on Twitter.

Methods

A corpus of tweets from Twitter, posted between January 2017 and November 2021, were identified by a Boolean search string (N = 149,343), where 48,695 tweets were classified as relevant by a machine learning algorithm. These relevant tweets were further screened and labeled as promotional or organic based on product URLs, usernames, keywords, or hashtags. Topic modeling using Dirichlet Allocation was then employed for identifying latent patterns of words among relevant tweets. The Social Networking Potential (SNP) score was employed for identifying influential accounts.

Results

Most relevant tweets (89%) were organic, non-promotional expressions about N-TBWs. Account users who only posted non-promotional tweets had a significantly higher SNP than those who only posted promotional tweets. Yet, neither of the two groups of account users consisted of known celebrities. Topic modeling revealed three broad groups of topics (7 in total) denoting the attributes of hemp N-TBWs, interest in non-hemp N-TBWs, and product marketing.

Conclusions

The large proportion of organic tweets is indicative of the nascency of N-TBWs, which will need to be marketed more extensively if they are to replace cigar products used by blunt smokers.

Keywords:

• Non-tobacco blunt wraps
• social media
• topic modeling
• machine learning
• harm reduction
• tobacco regulation
• cannabis regulation

Acknowledgments

We thank Paul McMurray, B.S. for coding the tweets for relevancy to the non-tobacco and traditional blunt wraps used to train the SVM classifier, and sharing his expert knowledge in interpreting findings from the topic modeling.

Declaration of interest

No conflicts are declared by the authors.

Data availability statement

All related data used in this study are publicly available in the openICPSR Project Repository (project openicpsr-182001) in .csv format, doi:10.3886/E182001V1.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the University of California’s Tobacco-Related Disease Research Program (TRDRP; Grant No. T31IP1678; Recipient: DST) and the National Science Foundation (NSF; Grant No. 2107150; Recipient: CL).