Research from the Politecnico di Milano demonstrates that artificial intelligence models can identify users affiliated with online conspiracy communities through distinctive linguistic patterns, even when those users engage with neutral topics. The study, authored by researchers from Politecnico di Milano, the Ecole Polytechnique Federale de Lausanne, and the CENTAI Institute, analyzed 500 million Reddit comments across more than twenty large communities to map the psycholinguistic footprint of conspiracy theory participants. Utilizing advanced natural language processing techniques, the team developed algorithms capable of distinguishing users with ties to the r/conspiracy subreddit from the broader Reddit population. The models achieved an average classification accuracy of 87 percent. Notably, the linguistic markers remained identifiable up to several years before individuals formally joined conspiracy-focused communities. The detected patterns consistently included elevated frequencies of anger, anxiety, and references to conflict, illness, and mortality, alongside a higher prevalence of emotionally charged and aggressive phrasing. A critical finding of the research is the absence of a uniform conspiratorial language. Instead, users dynamically adapt their communicative style to align with the social norms of different online environments. Consequently, the study demonstrates that models trained on community-specific data significantly outperform generalized platform-wide algorithms. This context-dependent adaptation underscores the limitations of broad-spectrum moderation tools and highlights the necessity for localized analysis frameworks. The primary manuscript has been accepted for presentation at the 2026 Annual Meeting of the Association for Computational Linguistics and is currently available on the arXiv preprint server. Building upon these initial results, the research team has completed a follow-up investigation accepted at the 2026 AAAI Conference on Web and Social Media. This secondary study examines community dynamics during periods of heightened mainstream visibility, specifically analyzing the r/conspiracy subreddit during the Jeffrey Epstein case surge. The findings indicate that increased exposure attracts transient engagement rather than fostering long-term user integration. These publications contribute substantive evidence to the field of computational social science, offering actionable insights for platform governance and content moderation. By mapping the subtle linguistic shifts associated with radicalization pathways, the research provides a foundation for developing more nuanced monitoring systems that account for contextual variation. As online ecosystems continue to evolve, context-aware AI tools will be essential for identifying harmful discourse while preserving legitimate cross-community dialogue.