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While digital trace data from sources like search engines hold enormouspotential for tracking and understanding human behavior, these streams of datalack information about the actual experiences of those individuals generatingthe data. Moreover, most current methods ignore or under-utilize humanprocessing capabilities that allow humans to solve problems not yet solvable bycomputers (human computation). We demonstrate how behavioral research, linkingdigital and real-world behavior, along with human computation, can be utilizedto improve the performance of studies using digital data streams. This studylooks at the use of search data to track prevalence of Influenza-Like Illness(ILI). We build a behavioral model of flu search based on survey data linked tousers online browsing data. We then utilize human computation for classifyingsearch strings. Leveraging these resources, we construct a tracking model ofILI prevalence that outperforms strong historical benchmarks using only alimited stream of search data and lends itself to tracking ILI in smallergeographic units. While this paper only addresses searches related to ILI, themethod we describe has potential for tracking a broad set of phenomena in nearreal-time.

Survey Data and Human Computation for Improved Flu Tracking