TY - GEN
T1 - Examining patterns of influenza vaccination in social media
AU - Huang, Xiaolei
AU - Smith, Michael C.
AU - Paul, Michael J.
AU - Ryzhkov, Dmytro
AU - Quinn, Sandra C.
AU - Broniatowski, David A.
AU - Dredze, Mark
N1 - Publisher Copyright:
© 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2017
Y1 - 2017
N2 - Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population's vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.
AB - Traditional data on influenza vaccination has several limitations: high cost, limited coverage of underrepresented groups, and low sensitivity to emerging public health issues. Social media, such as Twitter, provide an alternative way to understand a population's vaccination-related opinions and behaviors. In this study, we build and employ several natural language classifiers to examine and analyze behavioral patterns regarding influenza vaccination in Twitter across three dimensions: temporality (by week and month), geography (by US region), and demography (by gender). Our best results are highly correlated official government data, with a correlation over 0.90, providing validation of our approach. We then suggest a number of directions for future work.
UR - http://www.scopus.com/inward/record.url?scp=85027695856&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027695856&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85027695856
T3 - AAAI Workshop - Technical Report
SP - 542
EP - 546
BT - WS-17-01
PB - AI Access Foundation
T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017
Y2 - 4 February 2017 through 5 February 2017
ER -