Comparing covariation among vaccine hesitancy and broader beliefs within Twitter and survey data.
Comparing covariation among vaccine hesitancy and broader beliefs within Twitter and survey data.
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DOI：
10.1371/journal.pone.0239826
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Matthews LJ
中科院分区：
文献类型：
--
作者：
Nowak SA;Chen C;Parker AM;Gidengil CA;Matthews LJ
在过去的十年里，美国使用某种形式的社交媒体的成年人比例大约翻了一番，从2009年初的36%增加到2019年的72%。针对理解网上表达的观点和信仰的研究也相应增加。然而，社交媒体研究结果的普遍性是一个持续争论的话题。社交媒体平台是关于疫苗和疫苗犹豫的信息和错误信息的渠道。我们的研究目标是检验我们是否可以从推特和全国调查数据中得出类似的结论，说明疫苗犹豫与更广泛的信念之间的关系。2018年，我们在一篇文献综述的基础上，对美国父母进行了一项具有全国代表性的调查，询问他们对一系列主题的看法，包括疫苗副作用、阴谋论和对科学的理解。我们开发了一组基于关键字的查询，对应于调查中的每个信念项目，并从2017年提取匹配的推文。我们对2018年的最新全年数据进行了数据提取。我们的信念共变的主要措施是通过主成分分析（PCA）从两个来源获得的第一主成分的负荷和分数。我们发现，在使用推文中手动编码的网络链接来推断立场后，第一主成分加载与使用调查和Twitter数据的得分之间存在良好的定性一致性。在我们采取了额外的处理步骤，即根据个人发布的主题数量对Twitter数据进行重新采样后，这一结论仍然成立，这是一种纠正诱导（调查）与自愿（Twitter）信念差异表征的方法。总的来说，结果表明，使用Twitter数据的分析可能在某些情况下是可推广的，例如评估信念协变。
Over the past decade, the percentage of adults in the United States who use some form of social media has roughly doubled, increasing from 36 percent in early 2009 to 72 percent in 2019. There has been a corresponding increase in research aimed at understanding opinions and beliefs that are expressed online. However, the generalizability of findings from social media research is a subject of ongoing debate. Social media platforms are conduits of both information and misinformation about vaccines and vaccine hesitancy. Our research objective was to examine whether we can draw similar conclusions from Twitter and national survey data about the relationship between vaccine hesitancy and a broader set of beliefs. In 2018 we conducted a nationally representative survey of parents in the United States informed by a literature review to ask their views on a range of topics, including vaccine side effects, conspiracy theories, and understanding of science. We developed a set of keyword-based queries corresponding to each of the belief items from the survey and pulled matching tweets from 2017. We performed the data pull of the most recent full year of data in 2018. Our primary measures of belief covariation were the loadings and scores of the first principal components obtained using principal component analysis (PCA) from the two sources. We found that, after using manually coded weblinks in tweets to infer stance, there was good qualitative agreement between the first principal component loadings and scores using survey and Twitter data. This held true after we took the additional processing step of resampling the Twitter data based on the number of topics that an individual tweeted about, as a means of correcting for differential representation for elicited (survey) vs. volunteered (Twitter) beliefs. Overall, the results show that analyses using Twitter data may be generalizable in certain contexts, such as assessing belief covariation.
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