Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.
Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.
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DOI：
10.2196/jmir.9366
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发表时间：
2018-11-06
影响因子：
7.4
通讯作者：
Tsagarakis KP
中科院分区：
文献类型：
--
作者：
Mavragani A;Ochoa G;Tsagarakis KP
在信息过载的时代，大数据分析是获取和更好管理现有知识的答案吗？在过去的十年里，基于网络的数据在公共卫生问题上的使用，也就是信息学，已经被证明在评估人类行为的各个方面是有用的。谷歌趋势是收集这类信息的最受欢迎的工具，到目前为止，它已经被用于几个主题，其中健康和医学是最受关注的主题。对基于网络的行为进行监控和分析，以便检查实际的人类行为，从而预测、更好地评估、甚至预防日常生活中不断出现的与健康相关的问题。这项系统的回顾旨在报告和进一步介绍和分析2006年至2016年谷歌趋势(信息学)在健康相关主题中的研究方法、工具和统计方法，以提供对该工具的有用性的概述，并为未来对该主题的研究提供参考。按照系统综述和Meta分析选择研究的首选报告项目，我们在Scopus和PubMed数据库中搜索了2006-2016年间的术语“Google Trends”，对出版物和主题的类型应用了特定的标准。总共摘录了109篇已发表的论文，排除了重复和那些不属于健康和医学主题或选定的文章类型的论文。然后，我们根据已发表的论文的方法论方法，即可视化、季节性、相关性、预测和建模，进一步对其进行分类。根据定义，所有被检查的论文都包括时间序列分析，除两篇外，所有论文都包括数据可视化。共有23.1%(24/104)的研究使用谷歌趋势数据来检查季节性，39.4%(41/104)和32.7%(34/104)的研究分别使用相关性和建模。只有8.7%(9/104)的研究使用谷歌趋势数据来预测和预测与健康相关的主题；因此，使用谷歌趋势数据进行预测显然存在差距。对在线查询的监控可以提供对人类行为的洞察，因为这个领域正在显著且持续地增长，并将在未来被证明更有价值，用于评估行为变化，并为使用其他方式无法访问的数据进行研究提供基础。
In the era of information overload, are big data analytics the answer to access and better manage available knowledge? Over the last decade, the use of Web-based data in public health issues, that is, infodemiology, has been proven useful in assessing various aspects of human behavior. Google Trends is the most popular tool to gather such information, and it has been used in several topics up to this point, with health and medicine being the most focused subject. Web-based behavior is monitored and analyzed in order to examine actual human behavior so as to predict, better assess, and even prevent health-related issues that constantly arise in everyday life. This systematic review aimed at reporting and further presenting and analyzing the methods, tools, and statistical approaches for Google Trends (infodemiology) studies in health-related topics from 2006 to 2016 to provide an overview of the usefulness of said tool and be a point of reference for future research on the subject. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for selecting studies, we searched for the term “Google Trends” in the Scopus and PubMed databases from 2006 to 2016, applying specific criteria for types of publications and topics. A total of 109 published papers were extracted, excluding duplicates and those that did not fall inside the topics of health and medicine or the selected article types. We then further categorized the published papers according to their methodological approach, namely, visualization, seasonality, correlations, forecasting, and modeling. All the examined papers comprised, by definition, time series analysis, and all but two included data visualization. A total of 23.1% (24/104) studies used Google Trends data for examining seasonality, while 39.4% (41/104) and 32.7% (34/104) of the studies used correlations and modeling, respectively. Only 8.7% (9/104) of the studies used Google Trends data for predictions and forecasting in health-related topics; therefore, it is evident that a gap exists in forecasting using Google Trends data. The monitoring of online queries can provide insight into human behavior, as this field is significantly and continuously growing and will be proven more than valuable in the future for assessing behavioral changes and providing ground for research using data that could not have been accessed otherwise.
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通讯作者：
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