Francesco Corso was hunting for a pattern hidden in plain sight: the linguistic fingerprints of conspiracy-minded Reddit users left not in shadowy corner communities, but in ordinary conversations about films, music, and cooking.
Researchers at Politecnico di Milano wanted to answer a deceptively simple question: Do people who believe in conspiracy theories talk differently than everyone else, even when discussing neutral topics? The answer, emerging from analysis of 500 million Reddit comments, suggests they do—with striking consistency. Using psycholinguistic tools and artificial intelligence, the team discovered that users active in r/conspiracy show distinctive linguistic patterns with 87% accuracy, even years before they explicitly join conspiracy communities.
The study, led by Francesco Corso and Francesco Pierri at Politecnico di Milano alongside Giuseppe Russo from École Polytechnique Fédérale de Lausanne and Gianmarco De Francisci Morales from CENTAI Institute, examined more than 20 large Reddit communities to spot what sets conspiracy-community members apart. The linguistic markers were consistent: greater presence of anger and anxiety, frequent references to conflict, illness and death, and more aggressive or emotionally charged language across all topics, not just conspiratorial ones.
"In this work, we wanted to understand whether involvement in conspiracy communities leaves recognizable linguistic traces even outside the spaces in which these theories are explicitly discussed," Corso explained. The answer was a resounding yes—suggesting that conspiratorial thinking may be woven into how people communicate fundamentally, not just surface-level.
Yet the research also revealed something equally important: there is no single "conspiratorial language" that works everywhere. Users are adaptive communicators. They shift their style depending on which online community they're in, which means models built specifically for individual communities detected these patterns far more effectively than any universal model could. This finding has practical implications for platform moderators and researchers designing systems to flag potential radicalization or misinformation spread.
The work has been accepted for presentation at ACL 2026, one of the world's leading conferences on artificial intelligence and natural language processing. The findings are particularly timely as platforms grapple with understanding how fringe beliefs spread and how to intervene before users fully embed themselves in conspiracy ecosystems.
Pierri underscored the broader significance: "Users adapt their way of expressing themselves to the norms of different online communities, and this makes it necessary to design analysis and moderation tools that are more sensitive to context." This insight challenges the notion that you can build one-size-fits-all detection systems.
The research team has already extended this work into a second study, accepted at the 20th AAAI International Conference on Web and Social Media, examining the Jeffrey Epstein case and how mainstream media attention affects conspiracy community recruitment. That work shows something equally nuanced: visibility can bring new users to conspiracy spaces, but exposure alone doesn't guarantee they'll stay or become true believers.
Together, these studies illuminate radicalization as a process far more textured than algorithms typically account for—one where linguistic patterns precede explicit membership, where context matters deeply, and where understanding how people actually talk to each other may be key to addressing the spread of conspiratorial narratives online.