A Comparative Analysis of E-Cigarette and Cigarette Posts on Instagram

An expenditure analysis revealing how Philip Morris advertisements coincide with tobacco policymaking in Switzerland

INTRODUCTION

Prior research has linked media tobacco promotion to increased tobacco use and favorable perceptions of tobacco products. Switzerland's tobacco lobby employs advertisements to sway policy decisions in its favor, yet no recent research has assessed this in detail. Our study aims to provide detailed estimates of tobacco industry (TI) advertisement costs, focusing on Philip Morris International (PMI) in Switzerland, and examine potential chronological links between TI advertisement campaigns and parliamentary discussions on tobacco bills. By spreading knowledge on this issue, we aim to support the development of future tobacco advertisement regulations.

METHODS

We conducted an expenditure analysis of tobacco-related press advertisements in Swiss print media published between August 2020 and August 2021, accessed through the media intelligence firm Argus Data Insights. Advertisement costs were estimated using publicly available data. We plotted expenditure sums of PMI against key parliamentary session dates featuring discussions on proposed tobacco control measures, such as tighter restrictions on advertising.

RESULTS

Over 12 months, 501 advertisements with tobacco-specific headlines were published in Swiss press media. Of these, 437 advertisements (87.22%) were linked to PMI. PMI accounted for 88.21% (CHF 6486969) of total advertisement expenditure. Notably, PMI advertisements coincided with key political sessions discussing tobacco legislation in parliament, with a limited presence outside these periods.

CONCLUSIONS

PMI advertisements were published parallel to key moments of parliamentary discussions, suggesting an attempt by TI to potentially influence discussions. Applying such an advertisement monitoring methodology helps understand the contextual conditions of public health in Switzerland. By analyzing TI advertisements in print media, we sought to highlight regulatory gaps and support the creation of stricter advertising regulations. We recommend continuing such research to strengthen tobacco control policymaking. Key public health efforts should include raising awareness of TI tactics, implementing a comprehensive ban on tobacco advertising, and strategically engaging with the media in tobacco control campaigns.

Characteristics of Adolescents' and Young Adults' Exposure to and Engagement with Nicotine and Tobacco Product Content on Social Media

To inform policy and messaging, this study examined characteristics of adolescents' and young adults' (AYAs') exposure to and engagement with nicotine and tobacco product (NTP) social media (SM) content. In this cross-sectional survey study, AYAs aged 13-26 (N=1,163) reported current NTP use, SM use frequency, and exposure to and engagement with SM content promoting and opposing NTP use (i.e. frequency, source[s], format[s], platform[s]). Participants who used NTPs (vs. did not use) were more likely to report having seen NTP content (p-values<.001). Prevalent sources were companies/brands (46.6%) and influencers (44.4%); prevalent formats were video (65.4%) and image (50.7%). Exposure to content promoting NTP use was prevalent on several popular platforms (e.g. TikTok, Instagram, Snapchat); exposure to content opposing NTP use was most prevalent on YouTube (75.8%). Among those reporting content engagement (i.e. liking, commenting on, or sharing NTP content; 34.6%), 57.2% engaged with influencer content. Participants reported engaging with content promoting and opposing NTP use on popular platforms (e.g. TikTok, Instagram, YouTube). Participants with (versus without) current NTP use were significantly more likely to use most SM platforms and to report NTP content exposure and engagement (p-values<.05). Results suggest that NTP education messaging and enforcement of platforms' content restrictions are needed.

Characterizing Anti-Vaping Posts for Effective Communication on Instagram Using Multimodal Deep Learning

INTRODUCTION

Instagram is a popular social networking platform for sharing photos with a large proportion of youth and young adult users. We aim to identify key features in anti-vaping Instagram image posts associated with high social media user engagement by artificial intelligence.

AIMS AND METHODS

We collected 8972 anti-vaping Instagram image posts and hand-coded 2200 Instagram images to identify nine image features such as warning signs and person-shown vaping. We utilized a deep-learning model, the OpenAI: contrastive language-image pre-training with ViT-B/32 as the backbone and a 5-fold cross-validation model evaluation, to extract similar features from the Instagram image and further trained logistic regression models for multilabel classification. Latent Dirichlet Allocation model and Valence Aware Dictionary and sEntiment Reasoner were used to extract the topics and sentiment from the captions. Negative binomial regression models were applied to identify features associated with the likes and comments count of posts.

RESULTS

Several features identified in anti-vaping Instagram image posts were significantly associated with high social media user engagement (likes or comments), such as educational warnings and warning signs. Instagram posts with captions about health risks associated with vaping received significantly more likes or comments than those about help quitting smoking or vaping. Compared to the model based on 2200 hand-coded Instagram image posts, more significant features have been identified from 8972 AI-labeled Instagram image posts.

CONCLUSION

Features identified from anti-vaping Instagram image posts will provide a potentially effective way to communicate with the public about the health effects of e-cigarette use.

IMPLICATIONS

Considering the increasing popularity of social media and the current vaping epidemic, especially among youth and young adults, it becomes necessary to understand e-cigarette-related content on social media. Although pro-vaping messages dominate social media, anti-vaping messages are limited and often have low user engagement. Using advanced deep-learning and statistical models, we identified several features in anti-vaping Instagram image posts significantly associated with high user engagement. Our findings provide a potential approach to effectively communicate with the public about the health risks of vaping to protect public health.