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

2026-01-25

AI Summary

This study addresses a critical gap in the literature by examining the interpersonal emotional harm inflicted on family members and friends by QAnon-related radicalization—a dimension largely overlooked in existing research. Analyzing 12,747 personal narratives from the Reddit community r/QAnonCasualties, the authors integrate BERTopic for topic modeling, LDA-based graph modeling, and large language model–assisted sentiment detection with regression analysis to propose the novel concept of “radicalization personas.” Conceptualizing radicalization as a relational phenomenon, the research identifies six distinct radicalization personas and demonstrates their significant predictive power over narrators’ reported emotions, including anger, disgust, fear, and sadness. This work thus provides the first empirical framework for understanding the interpersonal affective consequences of conspiracy-driven radicalization.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

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.

Problem

• conspiracy theories
• radicalization
• relational harms
• QAnon
• emotional toll

Innovation

• radicalization personas
• relational harm
• mixed-methods approach
• LLM-assisted emotion detection
• conspiracy theory