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Internet data has surfaced as a primary source for investigation of differentaspects of human behavior. A crucial step in such studies is finding a suitablecohort (i.e., a set of users) that shares a common trait of interest toresearchers. However, direct identification of users sharing this trait isoften impossible, as the data available to researchers is usually anonymized topreserve user privacy. To facilitate research on specific topics of interest,especially in medicine, we introduce an algorithm for identifying a trait ofinterest in anonymous users. We illustrate how a small set of labeled examples,together with statistical information about the entire population, can beaggregated to obtain labels on unseen examples. We validate our approach usinglabeled data from the political domain.  We provide two applications of the proposed algorithm to the medical domain.In the first, we demonstrate how to identify users whose search patternsindicate they might be suffering from certain types of cancer. In the second,we detail an algorithm to predict the distribution of diseases given theirincidence in a subset of the population at study, making it possible to predictdisease spread from partial epidemiological data.

Inferring individual attributes from search engine queries and auxiliary information