Using Innovative Machine Learning to Detect Organized Support and Opposition to E-cigarette Use Prevention Campaign Messaging on Twitter and TikTok

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Using Innovative Machine Learning to Detect Organized Support and Opposition to E-cigarette Use Prevention Campaign Messaging on Twitter and TikTok In recent years sharp increases in the consumption of non-cigarette tobacco products, including vaping products or electronic nicotine delivery systems (ENDS), have largely offset the significant decline in cigarette smoking rates in the US over the past five decades. In response, several state, local and federal tobacco control programs have developed prevention campaign messages for youth vaping and e-cigarette use. Some of these campaigns have also provoked significant opposition on social media from tobacco harm reduction advocates. Strong evidence supports the effectiveness of anti-smoking media campaigns in preventing youth cigarette smoking, but little research has examined the effects of campaigns that aim to prevent other types of tobacco use, such as anti-vaping campaigns. Further, previous evidence about prevention campaign effects did not take into account the current cluttered media environment, where campaign messages compete for audience attention with tobacco product promotion messages and both supportive and oppositional messaging about the campaigns themselves. Thus, social media interventions to prevent e-cigarette use are necessary but have unknown potential effects in a competitive communication environment characterized by an influx of ENDS product marketing and advocacy, potentially resulting in audience confusion and misinformation. Therefore, measuring engagement with e-cigarette use prevention campaigns on social media and understanding the patterns of such engagement and information flow represent important advancements in the science of prevention and lend evidential support for tobacco regulatory policy. The proposed project will assess the e-cigarette use prevention-related content amount, reach, and engagement on social media (Aim 1); identify sources and major themes associated with both supportive and oppositional messages using machine learning (Aim 2); and apply social network theories and approaches to examine patterns of social media users’ information seeking, exposure, dissemination, and gatekeeping in the campaign periods over time (Aim 3).