Fabian Tschofenig | Searcharxiv

arXiv2026

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.

cs.SI↗

arXiv2025

Emergent Directedness in Social Contagion

An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal modeling framework that (i) simulates the possible network pathways that emerge as contagions spread and (ii) identifies which edges and nodes are most impactful on diffusion across these possible pathways. This yields a surprising discovery. If people require exposure to multiple peers to adopt a contagion (a.k.a., 'complex contagions'), the pathways that emerge often only work in one direction. In fact, the more complex a contagion is, the more asymmetric its paths become. This emergent directedness problematizes canonical theories of how networks mediate contagion. Weak ties spanning network regions - widely thought to facilitate mutual influence and integration - prove to privilege the spread contagions from one community to the other. Emergent directedness also disproportionately channels complex contagions from the network periphery to the core, inverting standard centrality models. We demonstrate two practical applications. We show that emergent directedness accounts for unexplained nonlinearity in the effects of tie strength in a recent study of job diffusion over LinkedIn. Lastly, we show that network evolution is biased toward growing directed paths, but that cultural factors (e.g., triadic closure) can curtail this bias, with strategic implications for network building and behavioral interventions.

cs.SI↗