Malicious creativity in Telegram’s (software) anti-vaccination ecosystem
Profiling actors and early misinformation dynamics
Bibliographic Data
| ID | 19488366 |
|---|---|
| Authors | Aelita Skaržauskienė (0000-0003-1606-0676, Vilnius Gediminas Technical University), Monika Mačiulienė (0000-0002-8527-7468, Mykolas Romeris University), Gintarė Gulevičiūtė (0000-0003-1974-3982, Mykolas Romeris University), Asta Zelenkauskaite (0000-0001-5762-4605, Vilnius Gediminas Technical University), Aistė Diržytė (0000-0003-2057-3108, Mykolas Romeris University), Sergio D'Antonio Maceiras (Universidad Politécnica de Madrid) |
| Year | 2026 |
| Volume | 19 |
| Issue | 1 |
| Pages | 132-141 |
| Publication date | 2026-03-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Creativity Studies (JOURNAL) |
| Journal identifiers | ISSN: 2345-0487 • E-ISSN: 2345-0479 |
| Publisher | Vilnius Gediminas Technical University (PUBLISHER • LT) |
| DOI | 10.3846/cs.2026.22247 |
| OpenAlex | W7133689360 |
| Language | EN |
| References cited | 27 |
This study examines methodological challenges in collecting and analysing misinformation on Telegram (software) and develops a platform-sensitive conceptual framework for identifying malicious actors. Addressing gaps in existing research, the framework accounts for Telegram’s distinctive features, including limited moderation, privacy affordances, and channel-based dissemination. The study combines a structured literature review with the development and empirical testing of a four-dimensional framework encompassing creators, message content, target victims, and social context. The framework is applied to the anti-vaccination ecosystem on Telegram using a dataset of 7550 messages collected from 151 public channels and manually annotated. The results demonstrate both the analytical value of structured content-based approaches and their limitations in attributing malicious activity without behavioural and network-level data
Conceptual framework · Creativity · Dynamics (music) · Misinformation · Profiling (computer programming) · Social dynamics · Social media · Misinformation and Its Impacts · Public Relations and Crisis Communication · Spam and Phishing Detection
A Survey of Fake News
Misinformation, Disinformation, and Online Propaganda
Information credibility on twitter
Polarization of the vaccination debate on Facebook
The political economy of digital profiteering
Natural Stings
Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for Covid-19 pandemic crisis
Creative, Antagonistic, and Angry? Exploring the Roots of Malevolent Creativity with a Real‐World Idea Generation Task
Vacinas e desinformação
Mobilizing During the Covid-19 Pandemic
Memes Save Lives
Covid-19 Protesters and the Far Right on Telegram
| Citation velocity | historical |
|---|---|
| Highly cited | No |