A network-driven framework for bidimensional analysis of information dissemination on social media platforms.

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorOliveira, Geovana Silva de-
Autor(es): dc.creatorLobo, João Pedro-
Autor(es): dc.creatorVenâncio, Otávio-
Autor(es): dc.creatorVieira, Vinicius da Fonseca-
Autor(es): dc.creatorAlmeida, Jussara Marques de-
Autor(es): dc.creatorSilva, Ana P. C.-
Autor(es): dc.creatorFerreira, Ronan Silva-
Autor(es): dc.creatorFerreira, Carlos Henrique Gomes-
Data de aceite: dc.date.accessioned2026-08-11T11:23:38Z-
Data de disponibilização: dc.date.available2026-08-11T11:23:38Z-
Data de envio: dc.date.issued2026-04-07-
Data de envio: dc.date.issued2024-
Fonte completa do material: dc.identifierhttps://www.repositorio.ufop.br/handle/123456789/21359-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1187806-
Descrição: dc.descriptionNetwork modeling has become a foundational approach for analyzing information dissemination on social media platforms. Moreover, backbone extraction techniques, designed to isolate the most relevant structural patterns in noisy networks, have been widely employed to identify salient interactions, especially in the context of coordi- nated behavior and campaign detection. However, most of these approaches rely on a one-dimensional perspective, focusing primarily on interaction volume while neglecting temporal dynamics that are crucial to understanding how content spreads in real time. This limited view can obscure important distinctions between dissemination strategies that are fast but sparse or slow but voluminous. To address this gap, we introduce a bidimensional framework that integrates both interaction volume and speed, enabling a more comprehensive modeling of dissemination dynamics. Our approach applies state-of-the-art backbone extraction techniques independently to each dimension, classifying edges into four distinct dissemination profiles. This classification provides new analytical affordances for explor- ing both structural and textual patterns of information flow across social platforms. Applied to two case studies on Twitter/X and Telegram, the framework reveals contrasting dissemination strategies across platforms and shows how different edge classes contribute to the amplification of specific narratives. These findings advance the study of information dynamics by offering a finer-grained, multidimensional perspective on user interactions in complex social networks.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languagept_BR-
Direitos: dc.rightsaberto-
Direitos: dc.rightsThis work is under a Creative Commons Attribution 4.0 International License. Fonte: o PDF do artigo.-
Palavras-chave: dc.subjectSocial media platforms-
Palavras-chave: dc.subjectInformation dissemination-
Palavras-chave: dc.subjectNetwork modeling and analysis-
Palavras-chave: dc.subjectNetwork backbone extraction-
Título: dc.titleA network-driven framework for bidimensional analysis of information dissemination on social media platforms.-
Aparece nas coleções:Repositório Institucional - UFOP

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