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

Crowd-based Multi-Predicate Screening of Papers in Literature Reviews