Generalists and Specialists: Using Community Embeddings to Quantify Activity Diversity in Online Platforms

Abstract

In many online platforms, people must choose how broadly to allocate their energy. Should one concentrate on a narrow area of focus, and become a specialist, or apply oneself more broadly, and become a generalist? In this work, we propose a principled measure of how generalist or specialist a user is, and study behavior in online platforms through this lens. To do this, we construct highly accurate community embeddings that represent communities in a high-dimensional space. We develop sets of community analogies and use them to optimize our embeddings so that they encode community relationships extremely well. Based on these embeddings, we introduce a natural measure of activity diversity, the GS-score. Applying our embedding-based measure to online platforms, we observe a broad spectrum of user activity styles, from extreme specialists to extreme generalists, in both community membership on Reddit and programming contributions on GitHub. We find that activity diversity is related to many important phenomena of user behavior. For example, specialists are much more likely to stay in communities they contribute to, but generalists are much more likely to remain on platforms as a whole. We also find that generalists engage with significantly more diverse sets of users than specialists do. Furthermore, our methodology leads to a simple algorithm for community recommendation, matching state-of-the-art methods like collaborative filtering. Our methods and results introduce an important new dimension of online user behavior and shed light on many aspects of online platform use.