  In this work, we initiate the investigation of optimization opportunities in
collaborative crowdsourcing. Many popular applications, such as collaborative
document editing, sentence translation, or citizen science resort to this
special form of human-based computing, where, crowd workers with appropriate
skills and expertise are required to form groups to solve complex tasks.
Central to any collaborative crowdsourcing process is the aspect of successful
collaboration among the workers, which, for the first time, is formalized and
then optimized in this work. Our formalism considers two main
collaboration-related human factors, affinity and upper critical mass,
appropriately adapted from organizational science and social theories. Our
contributions are (a) proposing a comprehensive model for collaborative
crowdsourcing optimization, (b) rigorous theoretical analyses to understand the
hardness of the proposed problems, (c) an array of efficient exact and
approximation algorithms with provable theoretical guarantees. Finally, we
present a detailed set of experimental results stemming from two real-world
collaborative crowdsourcing application us- ing Amazon Mechanical Turk, as well
as conduct synthetic data analyses on scalability and qualitative aspects of
our proposed algorithms. Our experimental results successfully demonstrate the
efficacy of our proposed solutions.
