Harvesting Collective Intelligence: Temporal Behavior in Yahoo Answers

Abstract

When harvesting collective intelligence, a user wishes to maximize the accuracy and value of the acquired information without spending too much time collecting it. We empirically study how people behave when facing these conflicting objectives using data from Yahoo Answers, a community driven question-and-answer site. We take two complementary approaches. We first study how users behave when trying to maximize the amount of the acquired information, while minimizing the waiting time. We identify and quantify how question authors at Yahoo Answers trade off the number of answers they receive and the cost of waiting. We find that users are willing to wait more to obtain an additional answer when they have only received a small number of answers; this implies decreasing marginal returns in the amount of collected information. We also estimate the user's utility function from the data. Our second approach focuses on how users assess the qualities of the individual answers without explicitly considering the cost of waiting. We assume that users make a sequence of decisions, deciding to wait for an additional answer as long as the quality of the current answer exceeds some threshold. Under this model, the probability distribution for the number of answers that a question gets is an inverse Gaussian, which is a Zipf-like distribution. We use the data to validate this conclusion.