Biases in using social media data for public health surveillance: A scoping review
Biases in using social media data for public health surveillance: A scoping review
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DOI：
10.1016/j.ijmedinf.2022.104804
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发表时间：
2022-05
影响因子：
4.9
通讯作者：
Yunpeng Zhao;Xing He;Zheng Feng;S. Bost;M. Prosperi;Yonghui Wu;Yi Guo;Jiang Bian
中科院分区：
文献类型：
--
作者：
Yunpeng Zhao;Xing He;Zheng Feng;S. Bost;M. Prosperi;Yonghui Wu;Yi Guo;Jiang Bian
ObjectivesA landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance，through a scoping review.材料和方法根据最佳实践，我们检索了两个文献数据库（即，PubMed和Web of Science），并涵盖截至2021年7月发表的文献。通过两轮筛选（即，标题/摘要筛选，然后全文筛选），我们提取研究目的，分析方法，以及用于评估或解决不同的偏见从合格的article.ResultsWe确定了2,856篇文章，从两个数据库。在筛选过程之后，我们提取并综合了20项研究，这些研究在利用社交媒体数据进行公共卫生监测时评估或减轻了偏见。研究人员试图评估或解决几种不同类型的偏见，如人口统计偏见，关键词偏见和平台偏见。特别是，我们发现11项研究试图通过将社交媒体数据与其他数据源进行比较来衡量社交媒体数据研究结果的可靠性。讨论和结论我们综合了偏见的类型以及用于评估或解决偏见的方法使用社交媒体数据进行公共卫生监测的研究。我们发现，尽管大量使用社交媒体数据的出版物，但很少有研究考虑从数据收集到分析方法存在的各种偏见问题。忽视偏倚可能会扭曲研究结果并导致意想不到的后果，特别是在公共卫生监测领域。这些研究差距需要进一步更系统地调查。其他领域解决偏见的策略可以引入未来使用社交媒体数据的公共卫生监测系统。
ObjectivesA landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance, through a scoping review.Materials and MethodsFollowing best practices, we searched two literature databases (i.e., PubMed and Web of Science) and covered literature published up to July 2021. Through two rounds of screening (i.e., title/abstract screening, and then full-text screening), we extracted study objectives, analysis methods, and the methods used to assess or address the different biases from the eligible articles.ResultsWe identified a total of 2,856 articles from the two databases. After the screening processes, we extracted and synthesized 20 studies that either assessed or mitigated biases when leveraging social media data for public health surveillance. Researchers have tried to assess or address several different types of biases such as demographic bias, keyword bias, and platform bias. In particular, we found 11 studies that tried to measure the reliability of the research findings from social media data by comparing them with other data sources.Discussion and ConclusionWe synthesized the types of biases and the methods used to assess or address the biases in studies that use social media data for public health surveillance. We found very few studies, despite the large number of publications using social media data, considered the various bias issues that are present from data collection to analysis methods. Overlooking bias can distort the study results and lead to unintended consequences, especially in the field of public health surveillance. These research gaps warrant further investigations more systematically. Strategies from other fields for addressing biases can be introduced for future public health surveillance systems that use social media data.