TY - GEN
T1 - On the ground validation of online diagnosis with twitter and medical records
AU - Bodnar, Todd
AU - Barclay, Victoria C.
AU - Ram, Nilam
AU - Tucker, Conrad S.
AU - Salathé, Marcel
N1 - Publisher Copyright:
© Copyright 2014 by the International World Wide Web Conferences Steering Committee.
PY - 2014/4/7
Y1 - 2014/4/7
N2 - Social media has been considered as a data source for track- ing disease. However, most analyses are based on models that prioritize strong correlation with population-level dis- ease rates over determining whether or not specific individ- ual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detec- Tion at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individ- ual's publicly available Twitter data. We find that about half (17=35 = 48:57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By develop- ing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diag- nose an individual with greater than 99% accuracy even if she does not discuss her health.
AB - Social media has been considered as a data source for track- ing disease. However, most analyses are based on models that prioritize strong correlation with population-level dis- ease rates over determining whether or not specific individ- ual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detec- Tion at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individ- ual's publicly available Twitter data. We find that about half (17=35 = 48:57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By develop- ing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diag- nose an individual with greater than 99% accuracy even if she does not discuss her health.
UR - http://www.scopus.com/inward/record.url?scp=84977841553&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84977841553&partnerID=8YFLogxK
U2 - 10.1145/2567948.2579272
DO - 10.1145/2567948.2579272
M3 - Conference contribution
AN - SCOPUS:84977841553
T3 - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
SP - 651
EP - 656
BT - WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PB - Association for Computing Machinery, Inc
T2 - 23rd International Conference on World Wide Web, WWW 2014
Y2 - 7 April 2014 through 11 April 2014
ER -