DETECTING OPINION LEADERS IN A TELEGRAM NETWORK OF FORWARDED MESSAGES - Tucci, Giulia; Castro Gouveia, Fabio (AoIR Selected Papers of Internet Research, 2026)
This study introduces a framework to identify opinion leaders on Telegram, adapting a methodology developed for Twitter. The work is motivated by the need to examine influence dynamics on hybrid platforms like Telegram, where platform affordances can potentially facilitate the spread of disinformation and polarizing discourse. Specifically, the traceability of forwarded messages provides analytical possibilities for understanding these complex communication networks. The analysis involves a case study conducted with data extracted from public groups supporting Jair Bolsonaro’s presidential re-election campaign. Using network analysis techniques, the method maps key roles including influencers, conversation starters, active engagers, and network creators. Results highlight these actors’ roles in shaping information circulation within Telegram’s public groups and channels. The discussion emphasizes the framework’s systematic approach to analyzing opinion leadership. This research contributes to understanding how hybrid social platforms facilitate influence and information dissemination, offering practical tools for researchers studying emerging social media ecosystems.