Compliance With FDA Nicotine Warning Statement Provisions in E-liquid Promotion Posts on Instagram

Abstract

Introduction Effective August 2018, the U.S. Food and Drug Administration required that nicotine addiction warnings be placed on ads for nicotine containing e-liquids. As per FDA comments, this provision pertains to visual ads communicated via social media, raising questions about compliance within the large e-liquid promotion community on Instagram. Methods This study examines use of warnings on promotional Instagram posts before and after provisions took effect on August 10th, 2018. Netlytic was used to gather a sample of 500 promotional #eliquid and #ejuice posts from: May 2017, October 2017, March 2018, August 2018, and September 2018. The 1,500-pre-warning and 1,000-post-warning posts were coded using content analysis. Changes in products and marketing strategies were also considered. Post volume was tracked monthly between May 2017 and February 2020. Results In the pre-warning period, nicotine warning statements were absent on all posts. Following August 10th, 2018, FDA compliant warnings were present on 13.6% of posts. Among US-based posts, 36.4% used the warnings, with warnings more common on posts made by e-liquid brands (52.3%) and posts promoting e-liquids with nicotine (40.0%). Promotional strategies and products did not significantly change. The share of posts made by U.S. Instagram users decreased by 11%, although total post volume continued to grow. Conclusions Many e-liquid promotion posts on Instagram remained non-compliant with nicotine warnings after FDA provisions took effect. The large volume of international users also limited the impact of FDA-mandated warnings on the social media environment. Implications Further guidance and enforcement are needed to ensure that U.S. e-liquid marketers on visual social media platforms adhere to current provisions, particularly for individual social media users who are sponsored by industry. The inherently global span of social media also indicates the importance of a shared approach to marketing regulations. Further work is needed to assess enforcement strategies viable for the social media environment.

E-cigarette brands and social media influencers on Instagram: a social network analysis

“…It is not possible to say with absolute certainty that all posts made by influencers were truly sponsored, since some influencers may also upload content that they were not incentivised to post about. This may overstate the number of sponsored posts, as well as complicate enforcement efforts 47…”

“…This may overstate the number of sponsored posts, as well as complicate enforcement efforts. 47 We only conducted a descriptive analysis and looked at the most frequently used measure in social network analysis, degree centrality. We did not assess the effect of exposure to promotional content on followers or product users.…”

Tracking e-cigarette warning label compliance on Instagram with deep learning

“…Helvetica or Arial bold) font size (The U.S. Food and Drug Administration 2020). Several studies (Vassey et al 2020;Laestadius et al 2020) evaluated compliance with the 2018 FDA requirements for warning labels on Instagram. Vassey et al (2020) manually reviewed 2,000 images posted in 2019 and found that only 7% included FDA-mandated warning statements.…”

“…Most of the international posts featured vaping products distributed in the U.S. and would therefore still be subject to compliance with the FDA warning-label regulations (Vassey et al 2020). Laestadius et al (2020) manually reviewed 1,000 posts collected in late 2018-early 2020 and found that only 13% included warning statements.…”

“…Both studies (Vassey et al 2020;Laestadius et al 2020) conducted qualitative analysis to assess the presence of warning statements on a small sample size of Instagram posts and used binary classification (presence or absence of a warning statement) without reporting warning labels that were too small or in the wrong place, which would constitute a partial compliance with the FDA requirements. This study addresses the limitation of prior research by developing an automated deep learning image classification capable of quickly and accurately measuring compliance with the FDA requirements for warning labels on a large sample size.…”