Survey Data and Human Computation for Improved Flu Tracking
While digital trace data from sources like search engines hold enormous potential for tracking and understanding human behavior, these streams of data lack information about the actual experiences of those individuals generating the data. Moreover, most current methods ignore or under-utilize human...
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| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
30-03-2020
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | While digital trace data from sources like search engines hold enormous
potential for tracking and understanding human behavior, these streams of data
lack information about the actual experiences of those individuals generating
the data. Moreover, most current methods ignore or under-utilize human
processing capabilities that allow humans to solve problems not yet solvable by
computers (human computation). We demonstrate how behavioral research, linking
digital and real-world behavior, along with human computation, can be utilized
to improve the performance of studies using digital data streams. This study
looks 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 to
users online browsing data. We then utilize human computation for classifying
search strings. Leveraging these resources, we construct a tracking model of
ILI prevalence that outperforms strong historical benchmarks using only a
limited stream of search data and lends itself to tracking ILI in smaller
geographic units. While this paper only addresses searches related to ILI, the
method we describe has potential for tracking a broad set of phenomena in near
real-time. |
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| DOI: | 10.48550/arxiv.2003.13822 |