On Quality Assesement in Wikipedia Articles Based on Markov Random Fields

This article investigates the possibility of accurate quality prediction of resources generated by communities based on the crowd-generated content. We use data from Wikipedia, the prime example of community-run site, as our object of study. We define the quality as a distribution of user-assigned grades across a predefined range of possible scores and present a measure of distribution similarity to quantify the accuracy of a prediction. The proposed method of quality prediction is based on Markov Random Field and its Loopy Belief Propagation implementation. Based on our results, we highlight key problems in the approach as presented, as well as trade-offs caused by relying solely on network structure and characteristics, excluding metadata. The overall results of content quality prediction are promising in homophilic networks.