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

dc.contributor.author Dongo I. es_PE
dc.contributor.author Cardinale Y. es_PE
dc.contributor.author Aguilera A. es_PE
dc.contributor.author Martinez F. es_PE
dc.contributor.author Quintero Y. es_PE
dc.contributor.author Robayo G. es_PE
dc.contributor.author Cabeza D. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2021
dc.description This research was supported by the FONDO NACIONAL DE DESARROLLO CIENTÍFICO, TECNOLÓGICO Y DE INNOVACIÓN TECNOLÓGICA – FONDECYT as executing entity of CONCYTEC under grant agreement no. 01–2019-FONDECYT-BM-INC.INV in the project RUTAS: Robots para centros Urbanos Turísticos Autónomos y basados en Semántica.
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. © 2021, Emerald Publishing Limited.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1108/IJWIS-03-2021-0037
dc.identifier.scopus 2-s2.0-85111661872
dc.identifier.uri https://hdl.handle.net/20.500.12390/2961
dc.language.iso eng
dc.publisher Emerald Group Holdings Ltd.
dc.relation.ispartof International Journal of Web Information Systems
dc.rights info:eu-repo/semantics/openAccess
dc.subject Web scraping
dc.subject API es_PE
dc.subject Credibility es_PE
dc.subject Qualitative analysis es_PE
dc.subject Twitter es_PE
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#2.02.04
dc.title A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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