COVIDSensing: Social Sensing Strategy for the Management of the COVID-19 Crisis

被引:6
|
作者
Sepulveda, Alicia [1 ]
Perinan-Pascual, Carlos [1 ]
Munoz, Andres [2 ]
Martinez-Espana, Raquel [3 ]
Hernandez-Orallo, Enrique [4 ]
Cecilia, Jose M. [4 ]
机构
[1] Univ Politecn Valencia, Dept Appl Linguist, Valencia 46022, Spain
[2] Univ Cadiz, Dept Comp Sci, Cadiz 11003, Spain
[3] Univ Murcia, Dept Informat & Commun Engn, Murcia 30100, Spain
[4] Univ Politecn Valencia, Dept Comp Engn DISCA, Valencia 46022, Spain
关键词
social sensing; COVID-19; Natural Language Processing; Machine Learning; data analysis; ARTIFICIAL-INTELLIGENCE; MEDIA;
D O I
10.3390/electronics10243157
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 socio-economic 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.
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页数:17
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