Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization

Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is challenging because there is not enough content to attract a critical mass of active members. This paper examines methods for addressing this cold-start problem in datamining-bootstrappable communities by attracting non-members to contribute to the community. We make four contributions: 1) we characterize a set of communities that are “datamining-bootstrappable” and define the bootstrapping problem in terms of decision-theoretic optimization, 2) we estimate the model parameters in a case study involving the Open AI Resources website, 3) we demonstrate that non-members' predicted interest levels and request design are important features that can significantly affect the contribution rate, and 4) we ran a simulation experiment using data generated with the learned parameters and show that our decision-theoretic optimization algorithm can generate as much community utility when bootstrapping the community as our strongest baseline while issuing only 55% as many contribution requests.