  Systematic literature reviews (SLRs) are one of the most common and useful
form of scientific research and publication. Tens of thousands of SLRs are
published each year, and this rate is growing across all fields of science.
Performing an accurate, complete and unbiased SLR is however a difficult and
expensive endeavor. This is true in general for all phases of a literature
review, and in particular for the paper screening phase, where authors lter a
set of potentially in-scope papers based on a number of exclusion criteria. To
address the problem, in recent years the research community has began to
explore the use of the crowd to allow for a faster, accurate, cheaper and
unbiased screening of papers. Initial results show that crowdsourcing can be
effective, even for relatively complex reviews. In this paper we derive and
analyze a set of strategies for crowd-based screening, and show that an
adaptive strategy, that continuously re-assesses the statistical properties of
the problem to minimize the number of votes needed to take decisions for each
paper, significantly outperforms a number of non-adaptive approaches in terms
of cost and accuracy. We validate both applicability and results of the
approach through a set of crowdsourcing experiments, and discuss properties of
the problem and algorithms that we believe to be generally of interest for
classification problems where items are classified via a series of successive
tests (as it often happens in medicine).
