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

Sebastian Schultheiß1, Dirk Lewandowski1,2, Sonja von Mach1

  • 1Department of Information, Hamburg University of Applied Sciences, Hamburg, Germany.

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|June 22, 2023
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This study introduces a query sampling method using keyword research tools to improve the validity of studies relying on search engine data. It enhances data collection for more accurate insights into social phenomena.

Keywords:
Keyword research toolQueriesQuery setSearch engines

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Area of Science:

  • Social Sciences
  • Computational Social Science
  • Information Science

Background:

  • Studies on social phenomena often use search engine queries.
  • Existing query selection methods may not reflect actual user behavior, questioning study validity.
  • A systematic approach is needed to ensure search query representativeness.

Purpose of the Study:

  • To develop and validate a systematic approach for sampling search engine queries.
  • To enhance the representativeness and evidential value of studies using search query data.
  • To provide a flexible and implementable method for researchers.

Main Methods:

  • Developed a 'query sampler' approach utilizing keyword research tools for search engine marketing.
  • Generated large volumes of topic-related queries and their associated search volumes.
  • Empirically tested the approach using queries from two existing published studies.

Main Results:

  • The proposed approach significantly expanded the number of queries and total search volume compared to existing methods.
  • Demonstrated the approach's applicability and effectiveness in a real-world context.
  • Provided open-source code for querying the Google Ads API to facilitate implementation.

Conclusions:

  • The query sampler approach offers a robust method for collecting representative search engine query data.
  • This method can improve the accuracy and reliability of studies analyzing social phenomena through search data.
  • Further testing across diverse topics is recommended to address potential limitations like topic drift.