Mining and Predicting Temporal Patterns in the Quality Evolution of Wikipedia Articles

Abstract

Online open collaboration systems like Wikipedia are complex adaptive systems within which large numbers of individual agents and artifacts interact and co-evolve over time. A key issue in these systems is the quality of the co-created artifacts and the processes through which high-quality artifacts are produced. In this paper, we took a dynamic approach to uncover common patterns in the temporal evolution of 6,057 Wikipedia articles in the domains of roads, films, and battles. Using Dynamic Time Warping, an advanced time-series clustering method, we identified three distinctive growth patterns, namely, stalled, plateaued, and sustained. Multinomial logistic regressions to predict these different clusters suggest that the path that an article follows is determined by both its inherent attributes, such as topic importance, and the contribution and coordination of editors who collaborated on the article. Our results also suggest that different factors matter at different stages of an article’s life cycle.