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Geographic variations in electronic cigarette advertisements on Twitter in the United States.
OBJECTIVES: Studies have identified a proliferation of e-cigarette advertisements on Twitter. We investigate whether the prevalence of e-cigarette related advertising is associated with state tobacco regulations after taking socio-economic characteristics into account.
METHODS: We collected e-cigarette related tweets from July 23 to October 14, 2015 (n = 757,167) on Twitter. State regulations and smoking prevalence were provided by the Campaign for Tobacco-Free Kids program. The socio-economic data were provided by the American Community Survey.
RESULTS: The number of commercial tweets was 319,041/day with a high potential reach (830,495,700/day). The prevalence of commercial tweets varied significantly by US state. The higher prevalence of e-cigarette advertising was associated with states with better tobacco control impact (r = 0.54, p < 0.0001) and lower youth smoking prevalence (r = -0.39, p = 0.005). In the multivariate analysis, state tobacco control impact is significantly associated with the prevalence of commercial tweets (β = 0.03 ± 0.01, p = 0.02).
CONCLUSIONS: Policies at both the federal and state levels are needed to regulate the content of commercial tweets and mitigate the negative effect of social media advertisements.
METHODS: We collected e-cigarette related tweets from July 23 to October 14, 2015 (n = 757,167) on Twitter. State regulations and smoking prevalence were provided by the Campaign for Tobacco-Free Kids program. The socio-economic data were provided by the American Community Survey.
RESULTS: The number of commercial tweets was 319,041/day with a high potential reach (830,495,700/day). The prevalence of commercial tweets varied significantly by US state. The higher prevalence of e-cigarette advertising was associated with states with better tobacco control impact (r = 0.54, p < 0.0001) and lower youth smoking prevalence (r = -0.39, p = 0.005). In the multivariate analysis, state tobacco control impact is significantly associated with the prevalence of commercial tweets (β = 0.03 ± 0.01, p = 0.02).
CONCLUSIONS: Policies at both the federal and state levels are needed to regulate the content of commercial tweets and mitigate the negative effect of social media advertisements.
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