Reddit Deplatforming and Toxicity Dynamics on Generalist Voat Communities

TL;DR

This study investigates how deplatforming on a major platform reshapes receiving alt-tech spaces, focusing on Voat's generalist communities. Using six matched Voat–Reddit generalist pairs and longitudinal MADOC data (2013–2020), it combines network analysis, RoBERTa-based toxicity detection (ToxiGen), sentiment, and a Dynamic Reputation model to trace migration effects. It identifies a two-regime migration pattern: an initial Hostile Takeover through high-volume newcomer arrivals (2015–2018) that forms parallel structures and elevates toxicity, followed by a Toxic Equilibrium through integration into a flattened toxic core (2018–2020). newcomer hub centrality remains low (less than 5% reaching top-degree) while toxicity doubles, indicating peripheral diffusion—not hub capture—as the driver of transformation; the /v/funny case demonstrates sustainable yet more toxic communities, underscoring externalized costs to receiving platforms. The findings have practical governance implications, signaling a narrow intervention window after bans and suggesting volume-based mitigation strategies to curb pernicious norm diffusion on alt-tech platforms.

Abstract

Deplatforming, the permanent banning of entire communities, is a primary tool for content moderation on mainstream platforms. While prior research examines effects on banned communities or source platform health, the impact on alternative platforms that absorb displaced users remains understudied. We analyze four major Reddit ban waves (2015--2020) and their effects on generalist communities on Voat, asking how post-ban arrivals reshape community structure and through what mechanisms transformation occurs. Combining network analysis, toxicity detection, and dynamic reputation modeling, we identify two distinct regimes of migration impact: (1) Hostile Takeover (2015--2018), where post-ban arrival cohorts formed parallel social structures that bypassed existing community cores through sheer volume, and (2) Toxic Equilibrium (2018--2020), where the flattening of existing user hierarchy enabled newcomers to integrate into the now-dominant toxic community. Crucially, community transformation occurred through peripheral dynamics rather than hub capture: fewer than 5% of newcomers achieved central positions in most months, yet toxicity doubled. Migration structure also shaped outcomes: loosely organized communities dispersed into generalist spaces, while ideologically cohesive groups concentrated in dedicated enclaves. These findings suggest that receiving platforms face a narrow intervention window during the hostile takeover phase, after which toxic norms become self-sustaining.

Figures (7)

• Figure 1: Platform-level dynamics on Voat across generalist communities. Vertical dashed lines indicate ban events (FPH: fatpeoplehate, PG: Pizzagate, GA: GreatAwakening, TD: The_Donald). Panels show: (1) total active users with reputation $> 1$, (2) active users split by newcomer/existing status, (3) E-I Index measuring cohort segregation, (4) newcomer hub rate, (5) degree share ratio, (6) degree assortativity, (7) mean reputation with 4.5 sustainability threshold (dashed line), (8) mean toxicity on Voat, and (9) toxicity differential heatmap by event period.
• Figure 2: Dynamics of /v/funny (Voat) vs. /r/funny (Reddit). The progressive toxicity increase following Event A demonstrates the Hostile Takeover regime; the plateau after Event C marks the transition to Toxic Equilibrium.
• Figure 3: Dynamics of /v/gaming vs. /r/gaming. Sharp activity spike following Event A, sustained toxicity elevation through both regimes.
• Figure 4: Dynamics of /v/gifs vs. /r/gifs. Similar two-regime pattern.
• Figure 5: Dynamics of /v/pics vs. /r/pics.