On the ground validation of online diagnosis with twitter and medical records

Todd Bodnar, Victoria C. Barclay, Nilam Ram, Conrad S. Tucker, Marcel Salathé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages651-656
Number of pages6
ISBN (Electronic)9781450327459
DOIs
StatePublished - Apr 7 2014
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: Apr 7 2014Apr 11 2014

Publication series

NameWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web

Other

Other23rd International Conference on World Wide Web, WWW 2014
Country/TerritoryKorea, Republic of
CitySeoul
Period4/7/144/11/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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