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

Irvin Dongo (Electrical and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa, Peru and Univ. Bordeaux, ESTIA Institute of Technology, Bidart, France)
Yudith Cardinale (Electrical and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa, Peru and Dpto. de Computación y T.I. Universidad Simón Bolívar Biblioteca, Caracas, Venezuela)
Ana Aguilera (Escuela de Ingenieria Informatica, Universidad de Valparaiso, Valparaiso, Chile)
Fabiola Martinez (Universidad Simon Bolivar, Caracas, Venezuela)
Yuni Quintero (Universidad Simon Bolivar, Caracas, Venezuela)
German Robayo (Universidad Simon Bolivar, Caracas, Venezuela)
David Cabeza (Universidad Simon Bolivar, Caracas, Venezuela)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 3 August 2021

Issue publication date: 1 December 2021

544

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.

Keywords

Acknowledgements

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.

Citation

Dongo, 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-03-2021-0037

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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