ScraCOVID-19: Plataforma informativa de contenido digital mediante Scraping y almacenamiento NoSQL

Abstract

Introduction— Keeping the community informed about the recent pandemic caused by COVID-19 has become a necessity, making the use of reliable communication channels accurate and evidence-based information indispensable.

Objectives— His work has as main objective to create ScraCOVID-19 on a connected digital content web platform to access updated news quickly. As a case study, four digital media are managed with national license. The news is presented in a summarized way to allow readers, depending on their interest, to read the news through some filters such as: unemployment, education, abuse, corruption and discrimination.

Methodology— ScraCOVID-19 is created from the Scraping extraction technique, using BeautifulSoup, a library that allows information in HTML format to be extracted from various websites, using the Python programming language. Results: As a result, a categorization model is described that extracts useful information to classify information into categories by referring to the URL.

Conclusions— It is concluded that, from extraction techniques used in conjunction with unstructured data storage tools, information is obtained from different web pages and all the data collected on the same dynamically generated web is managed.