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.accessioned2022-11-30T02:46:18Z
dc.date.available2022-11-30T02:46:18Z
dc.date.issued2021
dc.description.abstractPurpose – 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.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameDongo_Int2021.pdf
dc.identifier.citationDongo, I., Cardinale, Y., Aguilera, A., Martinez, F., Quintero, Y., Robayo, G. and Cabeza, D. (2021), "A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis", International Journal of Web Information Systems, Vol. 17 No. 6, pp. 580-606. https://doi.org/10.1108/IJWIS-03-2021-0037en_ES
dc.identifier.doihttps://doi.org/10.1108/IJWIS-03-2021-0037
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7312
dc.languageen
dc.publisherEmerald
dc.sourceInternational Journal of Web Information Systems
dc.subjectAPI, WEB SCRAPINGen_ES
dc.subjectTWITTERen_ES
dc.subjectCREDIBILITYen_ES
dc.subjectQUALITATIVE ANALYSISen_ES
dc.titleA qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
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