T-CREo: A Twitter Credibility Analysis Framework

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dc.contributor.authorCardinale, Yudith|Dongo, Irvin
dc.contributor.authorRobayo, Germán
dc.contributor.authorCabeza, David
dc.contributor.authorAguilera, Ana
dc.contributor.authorMedina, Sergio
dc.date.accessioned2022-11-30T02:46:14Z
dc.date.available2022-11-30T02:46:14Z
dc.date.issued2021
dc.description.abstractSocial media and other platforms on Internet are commonly used to communicate and generate information. In many cases, this information is not validated, which makes it difficult to use and analyze. Although there exist studies focused on information validation, most of them are limited to specific scenarios. Thus, a more general and flexible architecture is needed, that can be adapted to user/developer requirements and be independent of the social media platform. We propose a framework to automatically and in real-time perform credibility analysis of posts on social media, based on three levels of credibility: Text, User, and Social. The general architecture of our framework is composed of a front-end, a light client proposed as a web plug-in for any browser; a back-end that implements the logic of the credibility model; and a third-party services module. We develop a first version of the proposed system, called T-CREo (Twitter CREdibility analysis framework) and evaluate its performance and scalability. In summary, the main contributions of this work are: the general framework design; a credibility model adaptable to various social networks, integrated into the framework; and T-CREo as a proof of concept that demonstrates the framework applicability and allows evaluating its performance for unstructured information sources; results show that T-CREo qualifies as a highly scalable real-time service. The future work includes the improvement of T-CREo implementation, to provide a robust architecture for the development of third-party applications, as well as the extension of the credibility model for considering bots detection, semantic analysis and multimedia analysis.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameCardinale_T-cr2021.pdf
dc.identifier.citationY. Cardinale, I. Dongo, G. Robayo, D. Cabeza, A. Aguilera and S. Medina, "T-CREo: A Twitter Credibility Analysis Framework," in IEEE Access, vol. 9, pp. 32498-32516, 2021, doi: 10.1109/ACCESS.2021.3060623.en_ES
dc.identifier.doi10.1109/ACCESS.2021.3060623.
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7268
dc.languageen
dc.publisherIEEE
dc.sourceIEEE Access
dc.subjectAPIen_ES
dc.subjectCREDIBILTYen_ES
dc.subjectFAKE NEWSen_ES
dc.subjectINFORMATION SOURCESen_ES
dc.subjectTWITTERen_ES
dc.subjectWEB SCRAPING.en_ES
dc.titleT-CREo: A Twitter Credibility Analysis Framework
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica

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