A computational analysis of Telegram's narrative affordances.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 27 04 2023
accepted: 16 10 2023
medline: 16 11 2023
pubmed: 14 11 2023
entrez: 14 11 2023
Statut: epublish

Résumé

This paper offers an empirical investigation of the narrative profiles afforded by public, one-way messaging channels on Telegram. We define these narrative profiles in terms of the contribution of messages to a thread of narrative continuity, and test the double hypothesis that 1) Telegram channels afford diverse narrative profiles, corresponding with distinct vernacular uses of the platform's features, and that 2) networks of Telegram channels sampled from thematically distinct seed channels lean towards distinct profiles. To this end, we analyse the textual contents of 2,724,187 messages from 492 public messaging channels spanning five thematic networks. Our computational method builds up the narrative profiles by scrolling down channels and classifying each message according to its narrative fit with the surrounding messages. We thus find that Telegram channels afford several distinct storytelling profiles, which tend to defy traditional notions of narrative coherence. We furthermore observe correspondences between the thematic orientations of channels and their narrative profiles, with a preference for disparate profiles in channels pertaining to conspiracy theories and far-right counterculture, a preference for coherent profiles in channels pertaining to cryptocurrencies, and mixed types in channels pertaining to disinformation about the war in Ukraine. These empirical observations thus inform our further theorization on how platform features allow users to construct and shape narratives online.

Identifiants

pubmed: 37963150
doi: 10.1371/journal.pone.0293508
pii: PONE-D-23-12746
pmc: PMC10645302
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0293508

Informations de copyright

Copyright: © 2023 Tom Willaert. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

IEEE Trans Vis Comput Graph. 2014 Dec;20(12):1983-92
pubmed: 26356912
Nat Hum Behav. 2023 Jan;7(1):74-101
pubmed: 36344657
Mem Mind Media. 2021 Dec 9;1:
pubmed: 36415623
PLoS One. 2020 Jun 16;15(6):e0233879
pubmed: 32544200
Front Big Data. 2021 Aug 10;4:718368
pubmed: 34447929
Proc Natl Acad Sci U S A. 2018 May 1;115(18):4607-4612
pubmed: 29666239

Auteurs

Tom Willaert (T)

Brussels School of Governance, Vrije Universiteit Brussel, Brussels, Belgium.

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Classifications MeSH