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Skip search results from other journals and go to results- 2 Journal of Medical Internet Research
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We used the open source real-time infoveillance of Twitter health messages (RITHM) framework [17] to collect approximately 2.1 million tweets matching ENDS-related keywords and hashtags (vape, vapes, vaper, vapers, vaping, vaped, e-cigarette, e-cigarettes, e-cig, e-cigs, ecig, ecigs, juul, juuls, juuling) over multiple time points from March 1, 2020, through June 30, 2020, as recommended by Lienemann et al [21].
JMIR Form Res 2022;6(4):e26335
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Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets
From 2011 to 2019, current e-cigarette use among US high school students increased from 1.5% to 27.5% [1], prompting the US Surgeon General to declare a youth vaping epidemic. An appealing aspect of e-cigarette use to adolescents was the availability of flavors [2]. Among this population, initial use of flavored e-cigarettes is associated with progression to current e-cigarette use [3].
J Med Internet Res 2022;24(3):e27894
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Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
More recently, vaping has been associated with e-cigarette or vaping product use–associated lung injury, which has caused hospitalization and even death [3,4]. There is a rising concern that vaping increases addiction among nonsmokers, especially adolescents [5], and many are unaware of the addictive potential until after they become nicotine dependent [6]. Thus, there is a strong need to measure and understand the risks, sentiments, and behavior related to vaping.
J Med Internet Res 2020;22(8):e17478
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