Predicting prevalence of influenza-like illness from geo-tagged tweets

26th International World Wide Web Conference 2017, WWW 2017 Companion

Volumn , Issue , 2017, Pages 1327-1334

  Zhang, Kewei ^a,b   Arablouei, Reza ^b   Jurdak, Raja ^a,b  

^a UNIVERSITY OF QUEENSLAND   (Australia)

^b CSIRO   (Australia)

Author keywords

Classification; Data mining; Disease modeling; Public health monitoring; Regression analysis; Twitter

Indexed keywords

CLASSIFICATION (OF INFORMATION); CORRELATION METHODS; DISEASES; ELECTRONIC DOCUMENT EXCHANGE; EPIDEMIOLOGY; INSPECTION; LARGE DATASET; POPULATION STATISTICS; REGRESSION ANALYSIS; SOCIAL NETWORKING (ONLINE); WORLD WIDE WEB;

DETECTION APPROACH; DISEASE MODELING; HEALTH MONITORING; LINEAR CORRELATION; LINEAR REGRESSION MODELS; PEARSON CORRELATION COEFFICIENTS; SOCIAL MEDIA DATUM; TWITTER;

DATA MINING;

EID: 85053632089     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3041021.3051150     Document Type: Conference Paper

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