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This paper proposes a “small” contextual analysis approach to big data and reports our experimental application of this approach in evaluating the effects of social distancing on focused subpopulations in U.S. society. We recognize the common and critical limitations of big data, especially the unrepresentativeness and the unpublished methodology of accessible datasets. Our proposed methodological approach is built upon recent works on data ontology, especially the recognition that big data are essentially remaining digital footprints of human life in need of additional data of contextual factors for valid and meaningful interpretation. It guides the selection and processing of big data to make big data small and structured and thus articulable with traditional social sciences data and usable to conventional social sciences methods. In our experimental case study, we apply our sampling strategy developed from traditional social science data to Google’s mobility dataset for our analysis using primarily a Difference In Difference (DID) model. The results of this case study are of timely value to policy evaluation and public decision-making in the pandemic. We call for more proactive methodological innovations that confront the critical limitations of accessible big data especially in times of urgent needs.

“Small” analysis of Big Data: An evaluation of the effects of social distancing in the United States