Research Note—Content and Collaboration: An Affiliation Network Approach to Information Quality in Online Peer Production Communities

Abstract

The 15-year history of collaboration on Wikipedia offers insight into how peer production communities create knowledge. In this research, we combine disparate content and collaboration approaches through a social network analysis approach known as an affiliation network. It captures both how knowledge is transferred in a peer production network and also the underlying skills possessed by its contributors in a single methodological approach. We test this approach on the Wikipedia articles dedicated to medical information developed in a subcommunity known as a WikiProject. Overall, we find that the position of an article in the affiliation network is associated with the quality of the article. We further investigate information quality through additional qualitative and quantitative approaches including expert coders using medical students, crowdsourcing using Amazon Mechanical Turk, and visualization using network graphs. A review by fourth-year medical students indicates that the Wikipedia quality rating is a reliable measure of information quality. Amazon Mechanical Turk ratings, however, are a less reliable measure of information quality, reflecting observable content characteristics such as article length and the number of references.