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Tracking Citizen's Concerns during COVID-19 Pandemic
被引:14
|作者:
Chun, Soon Ae
[1
,2
]
Li, Alen Chih-Yuan
[3
]
Toliyat, Amir
[4
]
Geller, James
[5
]
机构:
[1] NYU, Governance Lab, Brooklyn, NY 10012 USA
[2] CUNY Coll Staten Isl, Brooklyn, NY 10314 USA
[3] New Jersey Inst Technol, CS Dept, Newark, NJ 07102 USA
[4] CUNY, Grad Ctr, CS Program, New York, NY USA
[5] New Jersey Inst Technol, CS Dept, Newark, NJ USA
来源:
PROCEEDINGS OF THE 21ST ANNUAL INTERNATIONAL CONFERENCE ON DIGITAL GOVERNMENT RESEARCH, DGO 2020
|
2020年
关键词:
Public health;
Covid-19;
Twitter mining;
Degree of Concern;
health policy;
D O I:
10.1145/3396956.3397000
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
COVID-19, the disease caused by the Corona Virus, started from Wuhan, China, in late December 2019, and quickly swept the Asian countries with confirmed cases and deaths. Within two and half months, it started spreading to European countries, and to the US, triggering the pandemic declaration by WHO. Governments around the globe have declared a public health crisis in specific regions and nationwide, with drastic measures taken to contain the spread of the disease. Citizens in this public health crisis are going through a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, panic, etc., through directly and viscerally experiencing the disease spreading. We present an approach to measure and monitor citizens' concern levels using public sentiments in Twitter data. Our approach shows temporal and geographic spread of citizens' concerns during the COVID-19 public health crisis.
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页码:322 / 323
页数:2
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