Efficient Crowd Exploration of Large Networks

• DOI: 10.1145/3274293
• Publication Date: 2018-11-01T21:21:27Z
• Authors: Daniel Berenberg; James P. Bagrow
• Keywords: 0301 basic medicine; Social and Information Networks (cs.SI); FOS: Computer and information sciences; Computer Science - Computers and Society; 0303 health sciences; 03 medical and health sciences; Computers and Society (cs.CY); Computer Science - Human-Computer Interaction; Computer Science - Social and Information Networks; Human-Computer Interaction (cs.HC)

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 can 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. Worker interactions reveal important characteristics of causal perception and the generated network data can help improve our understanding of causality and causal inference.