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Using Internet Search Data to Produce State-level Measures: The Case of Tea Party Mobilization

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  • Joseph DiGrazia

Abstract

This study proposes using Internet search data from search engines like Google to produce state-level metrics that are useful in social science research. Generally, state-level research relies on demographic statistics, official statistics produced by government agencies, or aggregated survey data. However, each of these data sources has serious limitations in terms of both the availability of the data and its ability to capture important concepts. This study demonstrates how state-level Google search measures can be produced and then demonstrates the effectiveness of such measures in an empirical case: predicting state-level Tea Party movement mobilization. Drawing on existing studies of the Tea Party movement and theories of right-wing and conservative mobilization, state-level Google search measures for anti-immigrant sentiment and economic distress are developed and compared to traditional metrics that are typically used to measure these concepts, such as the unemployment rate and the international immigration rate in their ability to successfully predict Tea Party event counts. The results show that the Google search measures are effective in predicting Tea Party mobilization in a way that is consistent with existing theory, while the traditional measures are not.

Suggested Citation

  • Joseph DiGrazia, 2017. "Using Internet Search Data to Produce State-level Measures: The Case of Tea Party Mobilization," Sociological Methods & Research, , vol. 46(4), pages 898-925, November.
    • Handle: RePEc:sae:somere:v:46:y:2017:i:4:p:898-925
    DOI: 10.1177/0049124115610348
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    References listed on IDEAS

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