Among Us: Language of Conspiracy Theorists on Mainstream Reddit
For social media platforms and researchers, this work demonstrates that linguistic signals of conspiracy theorists are not uniform across communities, necessitating tailored detection and moderation strategies.
The study analyzes over 500 million Reddit comments to show that users active in conspiracy-focused communities exhibit distinctive linguistic patterns that allow machine learning models to distinguish them from general users with 87% accuracy, but these patterns are community-specific, with community-specific models outperforming global classifiers by up to 17 percentage points.
The interaction between fringe subcultures and mainstream online communities poses significant challenges for understanding discourse on social media. In this work, we investigate whether users active in conspiracy-focused communities exhibit detectable linguistic signatures when participating in general-interest spaces, such as news, humor, or hobbyist forums. We analyze a large-scale longitudinal dataset of over 500 million comments spanning 10 years of Reddit activity, examining the communication patterns of these users across diverse social contexts independent of the topics they discuss. We show that these users exhibit distinctive linguistic patterns that enable machine learning models to reliably distinguish them from the general population within individual communities (averaging 87\% accuracy across more than 20 binary classification tasks). Crucially, no single aggregate model captures these patterns across communities, as community-specific models outperform global classifiers by up to 17 percentage points. This result suggests that while these users are distinct, their linguistic expression is dynamic and highly responsive to the social norms of the environment they inhabit. Our findings suggest the need for tailored interventions in online spaces, as linguistic signals associated with conspiracy and fringe subcultures vary across communities and cannot be effectively addressed by uniform detection or moderation strategies.