Profiling antivaccination channels in Telegram: early efforts in detecting misinformation

Introduction Telegram’s privacy-focused architecture has made it a fertile ground for the spread of misinformation, yet its closed nature poses challenges for researchers. This study addresses the methodological gap in capturing and analysing misinformation on Telegram, with a particular focus on the anti-vaccination community. Methods The research was conducted in three phases: (1) a structured review of literature on misinformation dissemination via Telegram, (2) development of a conceptual framework incorporating features of message creators, message content, intended targets and broader social context, and (3) application of this framework to anti-vaccination Telegram channels using latent profile analysis (LPA). A dataset comprising 7,550 messages from 151 Telegram channels was manually annotated and analysed. Results LPA identified distinct profiles among the channels. Malicious and non-malicious channels showed significant differences in their communication patterns, particularly in the use of crisis framing, discursive manipulation, and thematic orientation. T-tests confirmed these distinctions. Discussion The findings highlight Telegram’s unique dynamics in misinformation spread and support the utility of the proposed framework in isolating harmful content. The study underscores the need for tailored analytical strategies for platforms with non-standard affordances and suggests that content-based profiling may assist in proactive moderation.