A test of time: Modeling the long-term success of crowd collaborations

The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to projects like Wikipedia. While some of these collaborative efforts see early success, their long-term success is key for lasting impact. Despite its importance, this dimension of success remains largely unexplored. Prior work has assessed the success of online collaborations, however, most approaches are time-agnostic, evaluating success without considering its longevity. Research on the factors that ensure the long-term preservation of high-quality standards in online collaboration is scarce. We address this gap. We propose a novel metric, "Sustainable Success," which measures the ability of collaborative efforts to maintain their quality over time. We introduce the SustainPedia dataset, which compiles data from 48.7K Wikipedia articles. All articles in SustainPedia have reached the highest indicators of quality provided by the English Wikipedia, but a portion of them (7.5%) were later demoted from this high-quality status. SustainPedia includes each article's label and more than 300 explanatory features such as edit history, user experience, and team composition. Using this dataset, we develop machine learning models to predict the sustainable success of Wikipedia articles. Our analysis reveals important insights. For example, we find that articles that take to be recognized as high-quality are more likely to maintain thier status over time (i.e. be sustainable). Additionally, user experience emerged as the most critical predictor of sustainability. Our model provides the opportunity of automatically flagging articles at risk of being unsustainable. It also supports actionable factors that are significantly associated with (un)sustainable Wikipedia articles.