Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization

TL;DR

This study investigates the interpersonal harms of online radicalization by analyzing 12,747 dual-narrative posts from r/QAnonCasualties to map OON radicalization trajectories, identify six data-driven personas, and link these personas to the emotional toll experienced by narrators. It combines BERTopic-based topic modeling, an LDA-inspired mixed-membership clustering, and LLM-assisted emotion detection, culminating in an ILR-based regression framework that ties persona composition to four negative emotions. The key contributions are (i) a three-phase trajectory model with 50 themes, (ii) a six-persona typology capturing diverse radicalization paths, and (iii) a quantitative link between persona type and narrator emotions, revealing distinct anger/disgust signatures for dispositional/ideological pathways and fear/sadness for personal-collapse trajectories. The findings advance understanding of radicalization as a relational phenomenon and offer actionable guidance for families, clinicians, and de-radicalization practitioners by aligning interventions with persona-driven harm profiles.

Abstract

The rise of conspiracy theories has created far-reaching societal harm in the public discourse by eroding trust and fueling polarization. Beyond this public impact lies a deeply personal toll on the friends and families of conspiracy believers, a dimension often overlooked in large-scale computational research. This study fills this gap by systematically mapping radicalization journeys and quantifying the associated emotional toll inflicted on loved ones. We use the prominent case of QAnon as a case study, analyzing 12747 narratives from the r/QAnonCasualties support community through a novel mixed-methods approach. First, we use topic modeling (BERTopic) to map the radicalization trajectories, identifying key pre-existing conditions, triggers, and post-radicalization characteristics. From this, we apply an LDA-based graphical model to uncover six recurring archetypes of QAnon adherents, which we term "radicalization personas." Finally, using LLM-assisted emotion detection and regression modeling, we link these personas to the specific emotional toll reported by narrators. Our findings reveal that these personas are not just descriptive; they are powerful predictors of the specific emotional harms experienced by narrators. Radicalization perceived as a deliberate ideological choice is associated with narrator anger and disgust, while those marked by personal and cognitive collapse are linked to fear and sadness. This work provides the first empirical framework for understanding radicalization as a relational phenomenon, offering a vital roadmap for researchers and practitioners to navigate its interpersonal fallout.

Figures (9)

• Figure 1: Sankey diagram showing radicalization trait flow across three stages: Pre-radicalization (left), Trigger (middle), and Post-radicalization (right). A connection indicates that two traits appeared in the same profile, and its width represents its overall frequency of co-occurrence. Within each column, topics are grouped by broader thematic categories (denoted by color blocks).
• Figure 2: Effects of persona balances on narrator emotions
• Figure 3: Distribution of post lengths (word counts).
• Figure 4: NMF vs LDA coherence score.
• Figure 5: An example of a "fractured" persona from the k=8 LDA model.