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Text Mining Approach to Analyze Coronavirus Impact: Mexico City as Case of Study
Josimar Edinson Chire Saire
ANABEL PINEDA BRISEÑO
Acceso Abierto
Atribución-NoComercial-SinDerivadas
https://doi.org/10.1101/2020.05.07.20094466
https://www.medrxiv.org/content/10.1101/2020.05.07.20094466v1
The epidemiological outbreak of a novel coronavirus (2019-nCoV or Covid-19) in China, and its rapid spread, gave rise to the first pandemic in the digital age. Derived from this fact that has surprised humanity, many countries started with different strategies in order to stop the infection. In this context, one of the greatest challenges for the scientific community is monitoring (real time) the global population to get immediate feedback of what is happening with the people during this public health contingency. An alternative interesting and affordable for the materialization of the aforementioned are the social networks. In a social network, the persons can act as sensors/information not only of personal data but also data derived from their behavior. This paper aims to analyze the publications of people in Mexico using a Text Mining approach. Specifically, Mexico City is presented as a case study to help understand the impact on society of the spread of Covid-19.
medRxiv and bioRxiv
12-05-2020
Preimpreso
www.medrxiv.org
Inglés
Epidemia COVID-19
Público en general
VIRUS RESPIRATORIOS
Versión publicada
publishedVersion - Versión publicada
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