Internet data has surfaced as a primary source for investigation of di\ufb00erent aspects of human behavior. A crucial step in such studies is \ufb01nding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. However, direct identi\ufb01cation of users sharing this trait is often impossible, as the data available to researchers is usually anonymized to preserve user privacy. To facilitate research on speci\ufb01c topics of interest, especially in medicine, we introduce an algorithm for identifying a trait of interest in anonymous users. We illustrate how a small set of labeled examples, together with statistical information about the entire population, can be aggregated to obtain labels on unseen examples. We validate our approach using labeled data from the political domain. We provide two applications of the proposed algorithm to the medical domain. In the \ufb01rst, we demonstrate how to identify users whose search patterns indicate they might be su\ufb00ering from certain types of cancer. This shows, for the \ufb01rst time, that search queries can be used as a screening device for diseases that are currently often discovered too late, because no early screening tests exists. In the second, we detail an algorithm to predict the distribution of diseases given their incidence in a subset of the population at study, making it possible to predict disease spread from partial epidemiological data.<\/p>\n","protected":false},"excerpt":{"rendered":"
Internet data has surfaced as a primary source for investigation of di\ufb00erent aspects of human behavior. A crucial step in such studies is \ufb01nding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. 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