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The search results were downloaded into Microsoft Excel from Radian6, including the date and time of the tweet, Twitter handle, the entire text of the tweet (including URLs, hashtags), and the number of followers at the time the tweet was posted. Twitter data can provide new insights on e-cigarettes to help inform future research, regulations, surveillance, and enforcement efforts.Prepositional phrases containing these words as objects were excluded from our analysis.Fourth, to extract prepositional phrases from the tweets, we extracted each preposition and all subsequent words before the next verb, preposition, or wh-” adverb (eg, whenever,” where”). Second, this study identifies marketing trends that may be indicative of spamming or fake consumers (eg, purchasing followers), which has important implications for prioritizing regulatory efforts for e-cigarette marketing.First, we selected a subset of the tweets containing the words smoking, vaping, smokes, vapes, smoked, and vaped that indicated active usage of e-cigarettes. We used text content of the tweet and 5 metadata features in our classification algorithm: follower count at time of tweet, following count at time of tweet, number of tweets at time of tweet, a binary feature indicating whether or not the tweet contained a URL, and a binary feature indicating whether or not the tweet was a retweet.E-cigarettes are currently not regulated by the US Food and Drug Administration (FDA), but the FDA has issued a deeming rule to extend their authority to regulate e-cigarettes. Researchers have begun to explore e-cigarette conversations on Twitter 27 - 29 Huang and colleagues 29 analyzed e-cigarette tweets from May to June 2012 and found that 90% of tweets were commercial/advertising-related and that these tweets emanated from a relatively small subset of extremely active users.

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