Effects of Algorithmic Visibility on Conspiracy Communities: Reddit after Epstein's 'Suicide'

Abstract

Following the death of Jeffrey Epstein, the subreddit r/conspiracy experienced a significant visibility shock that brought mainstream users into direct contact with established conspiracy narratives. In this work, we explore how large-scale surges in public attention reshape participation and discourse within online conspiracy communities. We ask whether a sudden increase in exposure changes who join r/conspiracy, how long they stay, and how they adapt linguistically, compared with users who arrive through organic discovery. Using a computational framework that combines toxicity scores, survival analysis, and lexical and semantic measures over a period of 12 months, we observe that mainstream visibility is is associated with patterns consistent with a selection mechanism rather than a simple amplifier. Users who join the conspiracy community during the arrest-period tend to show higher linguistic similarity to core users, especially regarding linguistic and thematic norms and showing more stable engagement over time. By contrast, users who arrive during the height of public visibility remain semantically distant from core discourse and participate more briefly. Overall, we find that mainstream visibility is connected with changes in audience size, community composition, and linguistic cohesion. However, incidental exposure during attention shocks does not typically produce durable, integrated community members. These results provide a more nuanced understanding of how external events and platform visibility influence the growth and evolution of conspiracy spaces, offering insights for the design of responsible and transparent recommendation systems.

Figures (8)

• Figure 1: Longitudinal cohort analysis showing weekly posting activity for each of the four user cohorts.
• Figure 2: Interrupted time series analysis of core users' toxicity. The blue line shows the observed daily toxicity estimate. The red segments show the fitted pre-event and post-event trends from the OLS regression. The vertical line at $t_0$ marks Epstein's death. One extreme spike above 7 standard deviations in June comes from a day with very few posts and one highly toxic message; this outlier does not drive the fitted trend because the bootstrap uses medians.
• Figure 3: Shift in Empath category of core users' posts spanning the arrest and the death of Jeffrey Epstein in r/conspiracy. Asterisks denote significance levels (***: $p < 0.001$; **: $p < 0.01$; *: $p < 0.05$).
• Figure 4: Shift in Empath category between core and new users' posts spanning the arrest and the death of Jeffrey Epstein in r/conspiracy. Asterisks denote significance levels (***: $p < 0.001$; **: $p < 0.01$; *: $p < 0.05$).
• Figure 5: Kaplan-Meier survival curves for the four newcomer cohorts. From top to bottom over most of the time range, the curves are: arrest--engaged, arrest--not-engaged, death--engaged, and death--not-engaged. Solid and dashed line styles distinguish cohorts in addition to colour for accessibility.