1 research outputs found
Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution
Accurately and efficiently crowdsourcing complex, open-ended tasks can be
difficult, as crowd participants tend to favor short, repetitive "microtasks".
We study the crowdsourcing of large networks where the crowd provides the
network topology via microtasks. Crowds can explore many types of social and
information networks, but we focus on the network of causal attributions, an
important network that signifies cause-and-effect relationships. We conduct
experiments on Amazon Mechanical Turk (AMT) testing how workers propose and
validate individual causal relationships and introduce a method for independent
crowd workers to explore large networks. The core of the method, Iterative
Pathway Refinement, is a theoretically-principled mechanism for efficient
exploration via microtasks. We evaluate the method using synthetic networks and
apply it on AMT to extract a large-scale causal attribution network, then
investigate the structure of this network as well as the activity patterns and
efficiency of the workers who constructed this network. Worker interactions
reveal important characteristics of causal perception and the network data they
generate can improve our understanding of causality and causal inference.Comment: 25 pages, 14 figures, in CSCW'1