Conspiracy Theory Rabbit Holes Emerge via Interacting Contagions
Why do people fall into conspiracy theory rabbit holes? Prior research explains rabbit holes via psychological and algorithmic causes, yielding inconsistent findings. Here, we argue that rabbit holes can also arise from interactions among conspiracy theories spreading as social contagions. Using 7.6 million tweets from 7,416 X users during the first wave of COVID-19, we identify public endorsement of 15 conspiracy narratives with prompt-tuned large language models. Sequential hazard models show that, characteristic of rabbit holes, adopting a conspiracy theory elevates the risk of sharing subsequent conspiracy theories, that this elevation grows and persists longer with the number of conspiracy theories shared, and that transitions between theories concentrate among semantically proximate narratives, revealing semantic interactions that mediate social contagions. We also document what we term the settler effect: a user's entry into a new semantic region is slower, but once entry occurs, subsequent within-region adoption accelerates. We compare a range of agent-based models in their ability to reproduce these dynamics. Neither independent adoption nor a generic post-adoption increase in susceptibility reproduces the joint temporal and semantic pattern of the settler effect; among the alternatives considered, an ecology-of-contagions model that formalizes belief-system reshaping most parsimoniously reproduces these patterns. Using counterfactual network simulations that account for interactions among conspiracy theories, we find that preventing the first public endorsement of a conspiracy theory can rival high-detection shadow banning and outperform week-long read-only lockouts at reducing the spread of conspiracy theories.