TY - GEN
T1 - Worldwide Influenza Surveillance through Twitter
AU - Paul, Michael J.
AU - Dredze, Mark
AU - Broniatowski, David A.
AU - Generous, Nicholas
N1 - Funding Information:
Michael Paul is supported by a Microsoft Research PhD fellowship. Approved for public release LA-UR-14-27933
Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015
Y1 - 2015
N2 - We evaluate the performance of Twitter-based influenza surveillance in ten English-speaking countries across four continents. We find that tweets are positively correlated with existing surveillance data provided by government agencies in these countries, with r values ranging from .37-81. We show that incorporating Twitter data into a strong autoregressive baseline reduces mean squared error in 80 to 100 percent of locations depending on the lag, with larger improvements when reporting delays are longer.
AB - We evaluate the performance of Twitter-based influenza surveillance in ten English-speaking countries across four continents. We find that tweets are positively correlated with existing surveillance data provided by government agencies in these countries, with r values ranging from .37-81. We show that incorporating Twitter data into a strong autoregressive baseline reduces mean squared error in 80 to 100 percent of locations depending on the lag, with larger improvements when reporting delays are longer.
UR - http://www.scopus.com/inward/record.url?scp=84964583059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964583059&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84964583059
T3 - AAAI Workshop - Technical Report
SP - 6
EP - 11
BT - The World Wide Web and Public Health Intelligence - Papers Presented at the 29th AAAI Conference on Artificial Intelligence, Technical Report
PB - AI Access Foundation
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015
Y2 - 25 January 2015 through 30 January 2015
ER -