Query sampler: generating query sets for analyzing search engines using keyword research tools

Search engine queries are the starting point for studies in different fields, such as health or political science. These studies usually aim to make statements about social phenomena. However, the queries used in the studies are often created rather unsystematically and do not correspond to actual user behavior. Therefore, the evidential value of the studies must be questioned. We address this problem by developing an approach (query sampler) to sample queries from commercial search engines, using keyword research tools designed to support search engine marketing. This allows us to generate large numbers of queries related to a given topic and derive information on how often each keyword is searched for, that is, the query volume. We empirically test our approach with queries from two published studies, and the results show that the number of queries and total search volume could be considerably expanded. Our approach has a wide range of applications for studies that seek to draw conclusions about social phenomena using search engine queries. The approach can be applied flexibly to different topics and is relatively straightforward to implement, as we provide the code for querying Google Ads API. Limitations are that the approach needs to be tested with a broader range of topics and thoroughly checked for problems with topic drift and the role of close variants provided by keyword research tools.

Automated summary

Search engine queries are often used in studies to make statements about social phenomena, but the queries used in these studies are usually not systematic and do not reflect actual user behavior. We developed a method called query sampler, which samples queries from commercial search engines using keyword research tools, to address this problem. Our approach allows for the generation of large numbers of queries related to a given topic and provides information on query volume. Empirical testing showed that our approach can significantly expand the number of queries and total search volume. It has wide applications for studies using search engine queries to draw conclusions about social phenomena.