Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization Shih-Wen Huang* Jonathan Bragg* Isaac Cowhey University of Washington University of Washington Allen Institute for AI Seattle, WA, USA Seattle, WA, USA Seattle, WA, USA
[email protected] [email protected] [email protected] Oren Etzioni Daniel S. Weld Allen Institute for AI University of Washington Seattle, WA, USA Seattle, WA, USA
[email protected] [email protected] ABSTRACT INTRODUCTION Successful online communities (e.g., Wikipedia, Yelp, and The Internet has spawned communities that create extraordi- StackOverflow) can produce valuable content. However, nary resources. For example, Wikipedia’s 23 million users many communities fail in their initial stages. Starting an on- have created over 4 million English articles, a resource over line community is challenging because there is not enough 100 times larger than any other encyclopedia. Similarly, content to attract a critical mass of active members. This StackOverflow has become a top resource for programmers paper examines methods for addressing this cold-start prob- with 14 million answers to 8.5 million questions, while Yelp lem in datamining-bootstrappable communities by attract- users generated more than 67 million reviews.1 ing non-members to contribute to the community. We make four contributions: 1) we characterize a set of communi- In reality, however, most online communities fail. For exam- ple, thousands of open source projects have been created on ties that are “datamining-bootstrappable” and define the boot- SourceForge, but only 10% have three or more members [24]. strapping problem in terms of decision-theoretic optimiza- Furthermore, more than 50% of email-based groups received tion, 2) we estimate the model parameters in a case study involving the Open AI Resources website, 3) we demonstrate no messages during a four-month study period [6].