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Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram
  1. Neal Shah1,2,
  2. Matthew Nali2,3,
  3. Cortni Bardier2,4,
  4. Jiawei Li2,
  5. James Maroulis4,
  6. Raphael Cuomo2,3,
  7. Tim K Mackey1,2,4
  1. 1Department of Healthcare Research and Policy, University of California San Diego, La Jolla, California, USA
  2. 2Global Health Policy and Data Institute, San Diego, California, USA
  3. 3Department of Anesthesiology, University of California San Diego School of Medicine, La Jolla, California, USA
  4. 4Global Health Program, Department of Anthropology, University of California San Diego, La Jolla, California, USA
  1. Correspondence to Professor Tim K Mackey, Global Health Policy and Data Institute, San Diego, USA; tmackey{at}ucsd.edu

Abstract

Background Increased public health and regulatory scrutiny concerning the youth vaping epidemic has led to greater attention to promotion and sales of vaping products on social media platforms.

Objectives We used unsupervised machine learning to identify and characterise sale offers of electronic nicotine delivery systems (ENDS) and associated products on Instagram. We examined types of sellers, geographic ENDS location and use of age verification.

Methods Our methodology was composed of three phases: data collection, topic modelling and content analysis. We used data mining approaches to query hashtags related to ENDS product use among young adults to collect Instagram posts. For topic modelling, we applied an unsupervised machine learning approach to thematically categorise and identify topic clusters associated with selling activity. Content analysis was then used to characterise offers for sale of ENDS products.

Results From 70 725 posts, we identified 3331 engaged in sale of ENDS products. Posts originated from 20 different countries and were roughly split between individual (46.3%) and retail sellers (43.4%), with linked online sellers (8.8%) representing a smaller volume. ENDS products most frequently offered for sale were flavoured e-liquids (53.0%) and vaping devices (20.5%). Online sellers offering flavoured e-liquids were less likely to use age verification at point of purchase (29% vs 64%) compared with other products.

Conclusions Instagram is a global venue for unregulated ENDS sales, including flavoured products, and access to websites lacking age verification. Such posts may violate Instagram’s policies and US federal and state law, necessitating more robust review and enforcement to prevent ENDS uptake and access.

  • tobacco industry
  • electronic nicotine delivery devices
  • social marketing

Data availability statement

Data are available on reasonable request. Data are available on reasonable request to authors subject to appropriate deidentification.

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Data availability statement

Data are available on reasonable request. Data are available on reasonable request to authors subject to appropriate deidentification.

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Footnotes

  • Twitter @tkmackey

  • Contributors JL and RC collected data. NS, MN, CB and TKM conducted manual annotation. NS, MC, CB, JL, RC and TKM conducted data analyses. All authors contributed to the design, formulation, drafting, completion and approval of the final manuscript. TKM acquired funding for the study. The guarantor, TKM, accepts full responsibility for the work and/or the conduct of the study, had access to the data, and controlled the decision to publish.

  • Funding This study was funded by the University of California Tobacco-related Disease Research Program award no. T29IP0384 and T31IP1928.

  • Disclaimer The opinions expressed are those of the authors alone.

  • Map disclaimer The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.

  • Competing interests MN, CB, JL, and TKM are employees of the startup company S-3 Research LLC. S-3 Research is a startup funded and currently supported by the National Institutes of Health – National Institute of Drug Abuse through a Small Business Innovation and Research contract for opioid-related social media research and technology commercialisation. Author reports no other conflict of interest associated with this manuscript.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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