  Crowdsourcing works by distributing many small tasks to large numbers of
workers, yet the true potential of crowdsourcing lies in workers doing more
than performing simple tasks---they can apply their experience and creativity
to provide new and unexpected information to the crowdsourcer. One such case is
when workers not only answer a crowdsourcer's questions but also contribute new
questions for subsequent crowd analysis, leading to a growing set of questions.
This growth creates an inherent bias for early questions since a question
introduced earlier by a worker can be answered by more subsequent workers than
a question introduced later. Here we study how to perform efficient
crowdsourcing with such growing question sets. By modeling question sets as
networks of interrelated questions, we introduce algorithms to help curtail the
growth bias by efficiently distributing workers between exploring new questions
and addressing current questions. Experiments and simulations demonstrate that
these algorithms can efficiently explore an unbounded set of questions without
losing confidence in crowd answers.
