@misc{10498/26435,
year = {2021},
month = {12},
url = {http://hdl.handle.net/10498/26435},
abstract = {The management of the COVID-19 pandemic has been shown to be critical for reducing
its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media
services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy
information. However, collectively, such opinion holders constitute a large critical mass dispersed
everywhere and with an immediate capacity for information transfer. The main goal of this article is
to present a novel methodological tool based on social sensing, called COVIDSensing. In particular,
this application serves to provide actionable information in real time for the management of the socioeconomic
and health crisis caused by COVID-19. This tool dynamically identifies socio-economic
problems of general interest through the analysis of people’s opinions on social networks. Moreover,
it tracks and predicts the evolution of the COVID-19 pandemic based on epidemiological figures
together with the social perceptions towards the disease. This article presents the case study of Spain
to illustrate the tool.},
publisher = {MDPI},
keywords = {social sensing},
keywords = {COVID-19},
keywords = {Natural Language Processing},
keywords = {Machine Learning},
keywords = {data analysis},
title = {COVIDSensing: Social Sensing Strategy for the Management of the COVID-19 Crisis},
author = {Sepúlveda, Alicia and Periñán-Pascual, Carlos and Muñoz Ortega, Andrés and Martínez-España, Raquel and Hernández-Orallo, Enrique and Cecilia, José M.},
}