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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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    1. Stephen R. G. Jones & W. Craig Riddell, 1999. "The Measurement of Unemployment: An Empirical Approach," Econometrica, Econometric Society, vol. 67(1), pages 147-162, January.
    2. Richard J. Ladle & Peter A. Todd & Ana. C. M. Malhado, 2012. "Assessing insularity in global science," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(3), pages 745-750, December.
    3. Hopkins, Daniel J., 2010. "Politicized Places: Explaining Where and When Immigrants Provoke Local Opposition," American Political Science Review, Cambridge University Press, vol. 104(1), pages 40-60, February.
    4. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    5. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    6. Stephens-Davidowitz, Seth, 2014. "The cost of racial animus on a black candidate: Evidence using Google search data," Journal of Public Economics, Elsevier, vol. 118(C), pages 26-40.
    7. Fernandes, António S.C., 2012. "Assessing the technology contribution to value added," Technological Forecasting and Social Change, Elsevier, vol. 79(2), pages 281-297.
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