Identifying Competition and Mutualism Between Online Groups
Nathan TeBlunthuis, Benjamin Mako Hill
TL;DR
This work shows that treating overlapping online groups as a single environmental niche can obscure the diversity of intergroup relationships. By contrasting population-ecology density-dependence with a new community-ecology approach that infers directed competition and mutualism networks from time-series data, the authors demonstrate that most interactions within closely related subreddit clusters are mutualistic. They introduce a VAR-based framework to estimate a community matrix $\mathbf{\Phi}$, compute impulse responses, and quantify overall interaction strength $\kappa$ and mean mutualism $\overline{m}$, revealing a predominance of mutualism across clusters. The findings improve short-horizon forecasting when ecological interactions are modeled and offer a foundation for designing online communities that leverage intergroup complementarities. The approach complements existing population-ecology results and provides actionable insight for platform designers seeking to foster healthy, interconnected online ecosystems.
Abstract
Platforms often host multiple online groups with overlapping topics and members. How can researchers and designers understand how related groups affect each other? Inspired by population ecology, prior research in social computing and human-computer interaction has studied related groups by correlating group size with degrees of overlap in content and membership, but has produced puzzling results: overlap is associated with competition in some contexts but with mutualism in others. We suggest that this inconsistency results from aggregating intergroup relationships into an overall environmental effect that obscures the diversity of competition and mutualism among related groups. Drawing on the framework of community ecology, we introduce a time-series method for inferring competition and mutualism. We then use this framework to inform a large-scale analysis of clusters of subreddits that all have high user overlap. We find that mutualism is more common than competition.
