A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis

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dc.contributor.authorDongo, Irvin
dc.contributor.authorCardinale, Yudith
dc.contributor.authorAguilera, Ana
dc.contributor.authorMartinez, Fabiola
dc.contributor.authorQuintero, Yuni
dc.contributor.authorRobayo, German
dc.contributor.authorCabeza, David
dc.date.accessioned2022-03-11T00:36:38Z
dc.date.available2022-03-11T00:36:38Z
dc.date.issued2021
dc.description.abstract"purpose: This paper aims to perform an exhaustive revision of relevant and recent related studies, which reveals that both extraction methods are currently used to analyze credibility on Twitter. Thus, there is clear evidence of the need of having different options to extract different data for this purpose. Nevertheless, none of these studies perform a comparative evaluation of both extraction techniques. Moreover, the authors extend a previous comparison, which uses a recent developed framework that offers both alternates of data extraction and implements a previously proposed credibility model, by adding a qualitative evaluation and a Twitter-Application Programming Interface (API) performance analysis from different locations. Design/methodology/approach: As one of the most popular social platforms, Twitter has been the focus of recent research aimed at analyzing the credibility of the shared information. To do so, several proposals use either Twitter API or Web scraping to extract the data to perform the analysis. Qualitative and quantitative evaluations are performed to discover the advantages and disadvantages of both extraction methods. Findings: The study demonstrates the differences in terms of accuracy and efficiency of both extraction methods and gives relevance to much more problems related to this area to pursue true transparency and legitimacy of information on the Web. Originality/value: Results report that some Twitter attributes cannot be retrieved by Web scraping. Both methods produce identical credibility values when a robust normalization process is applied to the text (i.e. tweet). Moreover, concerning the time performance, Web scraping is faster than Twitter API and it is more flexible in terms of obtaining data; however, Web scraping is very sensitive to website changes. Additionally, the response time of the Twitter API is proportional to the distance from the central server at San Francisco." es_PE
dc.description.uriTrabajo académicoes_PE
dc.identifier.doi10.1108/IJWIS-03-2021-0037es_PE
dc.identifier.issn17440084
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17079
dc.language.isoenges_PE
dc.publisherEmerald Group Holdings Ltd.es_PE
dc.publisher.countryPEes_PE
dc.relationinfo:eu-repo/semantics/articlees_PE
dc.relation.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85111661872&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=388854f699364393473c7d2625e8af59&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=61&citeCnt=0&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectAPIes_PE
dc.subjectCredibilityes_PE
dc.subjectQualitative analysises_PE
dc.subjectTwitteres_PE
dc.subjectWeb scrapinges_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.00es_PE
dc.titleA qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysises_PE
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.typehttps://purl.org/pe-repo/renati/type#trabajoAcademico
thesis.degree.disciplineIngeniería Electrónica y de Telecomunicacioneses_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ingeniería Eléctrica y Electrónicaes_PE
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