Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.
Supplementing a survey with respondent Twitter data to measure e-cigarette information exposure.
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用受访者 Twitter 数据补充调查，以衡量电子烟信息暴露程度。
DOI：
10.1080/1369118x.2019.1566484
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Chew,Rob
中科院分区：
文献类型：
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作者：
Murphy,Joe;Hsieh,YPatrick;Wenger,Michael;Kim,AnniceE;Chew,Rob
研究人员越来越多地使用社交媒体数据来了解个人的行为和观点。像Twitter这样的平台提供了对个人帖子、朋友和追随者网络以及他们所接触到的内容的访问。本文介绍了一项探索性研究的方法和结果，以补充调查数据与受访者的Twitter帖子，Twitter朋友和追随者的网络，以及他们接触到的有关电子烟的信息。在电子烟研究和其他受在线信息共享和曝光影响的主题中，Twitter的使用非常重要。此外，Twitter元数据提供了用户的朋友和追随者的直接测量，而不是调查自我报告。我们发现，Twitter元数据提供了类似的信息，Twitter网络规模的调查问题，而不会引起召回错误或其他测量问题。使用情感编码和机器学习方法，我们发现Twitter可以阐明难以通过调查测量的主题，如在线表达的意见和网络组成。我们提出并讨论了模型，预测受访者是否使用调查和Twitter数据对电子烟进行积极的推文，发现综合数据提供了比单独来源更广泛的措施。
Social media data are increasingly used by researchers to gain insights on individuals’ behaviors and opinions. Platforms like Twitter provide access to individuals’ postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents’ Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user’s friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents’ tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.