Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution

@article{Berenberg2018EfficientCE,
  title={Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution},
  author={Daniel Berenberg and James P. Bagrow},
  journal={PACMHCI},
  year={2018},
  volume={2},
  pages={24:1-24:25}
}
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… CONTINUE READING
Tweets
This paper has been referenced on Twitter 58 times. VIEW TWEETS