Collecting representative social media samples from a search engine by adaptive query generation

Virgile Landeiro, Aron Culotta

Abstract: Studies in computational social science often require collecting data about users via a search engine interface: a list of keywords is provided as a query to the interface and documents matching this query are returned. The validity of a study will hence critically depend on the representativeness of the data returned by the search engine. In this paper, we develop a multi-objective approach to build queries yielding documents that are both relevant to the study and representative of the larger population of documents. We then specify measures to evaluate the relevance and the representativeness of documents retrieved by a query system. Using these measures, we experiment on three real-world datasets and show that our method outperforms baselines commonly used to solve this data collection problem.