University of Michigan, USA.
KU Leuven, Belgium.
Res Social Adm Pharm. 2021 Jun;17(6):1091-1100. doi: 10.1016/j.sapharm.2020.08.007. Epub 2020 Aug 10.
The use of Twitter data for health-related research has been increasing over time. While the organic nature of the data offer new opportunities, the limited understanding of how and by whom the data are generated poses a challenge for advancing health-related research. Individual-level data linkage could shed light into the data generation mechanism.
This paper investigates whether consent to link survey data with Twitter public data is associated with sociodemographic and Twitter use pattern factors and whether consenters and non-consenters differ on health-related outcomes.
Data from three health related surveys that use probability samples of the target population were used: 1) A college population web survey in KU Leuven University, 2) An adult population web survey of the US population, and 3) A population face-to-face survey in the Kingdom of Saudi Arabia (KSA). In all surveys, respondents reported whether they have a Twitter account, and Twitter users were asked to provide consent for linking their survey responses to their public Twitter data.
Consent rate estimates from the two web surveys in Belgium and the US were 24% and 27% respectively. The face-to-face survey in KSA yielded a higher consent rate of 45%. In general, respondents' sociodemographic characteristics were not significantly associated with consent to link. However, more use of social media and reporting sensitive information in the survey were found to be significantly correlated with higher consent. Consenters and non-consenters were not found to be statistically different on any of the health related measures.
Very few differences were found between those who consented to link their survey data with their Twitter public data and those who did not. Modifiable design variables need to be investigated to maximize consent while maintaining balance between consenters and non-consenters.
随着时间的推移，越来越多的人开始使用 Twitter 数据进行与健康相关的研究。虽然数据的自然属性为研究提供了新的机会，但由于人们对数据的生成方式和生成人群了解有限，这给与健康相关的研究带来了挑战。个体层面的数据关联可以揭示数据生成机制。
本文旨在调查同意将调查数据与 Twitter 公共数据关联的人是否与社会人口统计学和 Twitter 使用模式因素相关，以及同意者和不同意者在健康相关结果上是否存在差异。
本研究使用了来自三个健康相关调查的数据，这些调查均使用目标人群的概率样本：1）鲁汶大学的大学生网络调查，2）美国成人网络调查，3）沙特阿拉伯的面对面调查。在所有调查中，受访者均报告了他们是否拥有 Twitter 账户，并且 Twitter 用户被要求同意将其调查回复与他们的公共 Twitter 数据关联。
来自比利时和美国的两项网络调查的同意率估计分别为 24%和 27%。沙特阿拉伯的面对面调查则产生了 45%的更高同意率。一般来说，受访者的社会人口统计学特征与同意关联没有显著相关性。然而，发现更多地使用社交媒体和在调查中报告敏感信息与更高的同意率显著相关。在任何健康相关指标上，同意者和不同意者之间均未发现统计学差异。
同意将调查数据与他们的 Twitter 公共数据关联的人与不同意的人之间几乎没有差异。需要调查可修改的设计变量，以在保持同意者和不同意者之间平衡的同时，最大限度地提高同意率。
