dc.date.accessioned2022-03-11T00:16:50Z
dc.date.accessioned2023-05-30T23:30:49Z
dc.date.available2022-03-11T00:16:50Z
dc.date.available2023-05-30T23:30:49Z
dc.date.created2022-03-11T00:16:50Z
dc.date.issued2021
dc.identifier21693536
dc.identifierhttp://hdl.handle.net/20.500.12590/17074
dc.identifier10.1109/ACCESS.2021.3060623
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6478851
dc.description.abstract"Social 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"
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.publisherPE
dc.relationhttps://www.scopus.com/record/display.uri?eid=2-s2.0-85111948670&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=c0147ee94c46e56e76c75f54bcad6ea5&sot=aff&sdt=cl&cluster=scopubyr%2c%222021%22%2ct&sl=48&s=AF-ID%28%22Universidad+Cat%c3%b3lica+San+Pablo%22+60105300%29&relpos=58&citeCnt=2&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1
dc.relationinfo:eu-repo/semantics/article
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceUniversidad Católica San Pablo
dc.sourceRepositorio Institucional - UCSP
dc.subjectAPI
dc.subjectCredibilty
dc.subjectFake news
dc.subjectInformation sources
dc.subjectTwitter
dc.subjectWeb scraping
dc.titleT-CrEO: A twitter credibility analysis frameworkT-CrEO: A twitter credibility analysis framework
dc.typeinfo:eu-repo/semantics/article


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