Autocompletion interfaces make crowd workers slower, but their use promotes response diversity
Abstract: Creative tasks such as ideation or question proposal are powerful applications of crowdsourcing, yet
the quantity of workers available for addressing practical problems is often insufficient. To enable scalable
crowdsourcing thus requires gaining all possible efficiency and information from available workers. One
option for text-focused tasks is to allow assistive technology, such as an autocompletion user interface
(AUI), to help workers input text responses. But support for the efficacy of AUIs is mixed. Here
we designed and conducted a randomized experiment where workers were asked to provide short text
responses to given questions. Our experimental goal was to determine if an AUI helps workers respond
more quickly and with improved consistency by mitigating typos and misspellings. Surprisingly, we
found that neither occurred: workers assigned to the AUI treatment were slower than those assigned to
the non-AUI control and their responses were more diverse, not less, than those of the control. Both
the lexical and semantic diversities of responses were higher, with the latter measured using word2vec.
A crowdsourcer interested in worker speed may want to avoid using an AUI, but using an AUI to boost
response diversity may be valuable to crowdsourcers interested in receiving as much novel information
from workers as possible.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).