A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis
| Autor: | David Cabeza, Yuni Quintero, Irvin Dongo, Ana Aguilera, German Robayo, Fabiola Martinez, Yudith Cardinale |
|---|---|
| Přispěvatelé: | Universidad Católica San Pablo (UCSP), ESTIA Recherche, Ecole Supérieure des Technologies Industrielles Avancées (ESTIA), Universidad Simon Bolivar (USB), Universidad de Valparaiso [Chile] |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science 02 engineering and technology computer.software_genre World Wide Web Qualitative analysis 020204 information systems Credibility 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] 020201 artificial intelligence & image processing computer Web scraping Information Systems |
| Zdroj: | International Journal of Web Information Systems International Journal of Web Information Systems, Emerald Publishing Limited, 2021, 17 (6), pp.580-606. ⟨10.1108/IJWIS-03-2021-0037⟩ |
| ISSN: | 1744-0084 |
| DOI: | 10.1108/ijwis-03-2021-0037 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|