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Translating verbose information needs into crisp search queries is aphenomenon that is ubiquitous but hardly understood. Insights into this processcould be valuable in several applications, including synthesizing largeprivacy-friendly query logs from public Web sources which are readily availableto the academic research community. In this work, we take a step towardsunderstanding query formulation by tapping into the rich potential of communityquestion answering (CQA) forums. Specifically, we sample natural language (NL)questions spanning diverse themes from the Stack Exchange platform, and conducta large-scale conversion experiment where crowdworkers submit search queriesthey would use when looking for equivalent information. We provide a carefulanalysis of this data, accounting for possible sources of bias duringconversion, along with insights into user-specific linguistic patterns andsearch behaviors. We release a dataset of 7,000 question-query pairs from thisstudy to facilitate further research on query understanding.

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions