Department of Allied Health Sciences, University of Connecticut, Storrs, CT.
Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.
Transl Behav Med. 2019 Jan 1;9(1):41-47. doi: 10.1093/tbm/iby011.
Twitter may be useful for learning about indoor tanning behavior and attitudes. The objective of this study was to analyze the content of tweets about indoor tanning to determine the extent to which tweets are posted by people who tan, and to characterize the topics of tweets. We extracted 4,691 unique tweets from Twitter using the terms "tanning bed" or "tanning salon" over 7 days in March 2016. We content analyzed a random selection of 1,000 tweets, double-coding 20% of tweets (κ = 0.74, 81% agreement). Most tweets (71%) were by tanners (n = 699 individuals) and included tweets expressing positive sentiment about tanning (57%), and reports of a negative tanning experience (17%), burning (15%), or sleeping in a tanning bed (9%). Four percent of tweets were by tanning salon employees. Tweets posted by people unlikely to be tanners (15%) included tweets mocking tanners (71%) and health warnings (29%). The term "tanning bed" had higher precision for identifying individuals who engage in indoor tanning than "tanning salon"; 77% versus 45% of tweets captured by these search terms were by individuals who engaged in indoor tanning, respectively. Extrapolating to the full data set of 4,691 tweets, findings suggest that an average of 468 individuals who engage in indoor tanning can be identified by their tweets per day. The majority of tweets were from tanners and included reports of especially risky habits (e.g., burning, falling asleep). Twitter provides opportunity to identify indoor tanners and examine conversations about indoor tanning.
Twitter 可能有助于了解室内晒黑行为和态度。本研究的目的是分析有关室内晒黑的推文内容,以确定有多少推文是由晒黑者发布的,并描述推文的主题。我们使用“晒黑床”或“晒黑沙龙”等术语,在 2016 年 3 月的 7 天内从 Twitter 上提取了 4691 条唯一的推文。我们对 1000 条推文进行了随机选择的内容分析,对 20%的推文进行了双编码(κ=0.74,81%的一致性)。大多数推文(71%)是由晒黑者(n=699 人)发布的,其中包括对晒黑表示积极态度的推文(57%)、负面晒黑体验报告(17%)、晒伤(15%)或在晒黑床睡觉(9%)。4%的推文来自晒黑沙龙的员工。不太可能是晒黑者的人发布的推文(15%)包括对晒黑者的嘲讽推文(71%)和健康警告推文(29%)。“晒黑床”这个术语比“晒黑沙龙”更能准确识别参与室内晒黑的人;这两个搜索词捕捉到的推文分别有 77%和 45%是由参与室内晒黑的人发布的。根据 4691 条推文的完整数据集推断,平均每天有 468 名参与室内晒黑的人可以通过他们的推文识别出来。大多数推文来自晒黑者,其中包括特别危险的习惯(如晒伤、入睡)的报告。Twitter 提供了一个识别室内晒黑者并检查室内晒黑相关对话的机会。