Real-Time disease surveillance using twitter data:Demonstration on flu and cancer

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

134 Scopus citations

Abstract

Social media is producing massive amounts of data on an un- precedented scale. Here people share their experiences and opinions on various topics, including personal health issues, symptoms, treatments, side-effects, and so on. This makes publicly available social media data an invaluable resource for mining interesting and actionable healthcare insights. In this paper, we describe a novel real-Time u and cancer surveillance system that uses spatial, temporal, and text mining on Twitter data. The real-Time analysis results are reported visually in terms of US disease surveillance maps, distribution and timelines of disease types, symptoms, and treatments, in addition to overall disease activity timelines on our project website. Our surveillance system can be very useful not only for early prediction of seasonal disease out- breaks such as u, but also for monitoring distribution of cancer patients with different cancer types and symptoms in each state and the popularity of treatments used. The resulting insights are expected to help facilitate faster response to and preparation for epidemics and also be very useful for both patients and doctors to make more informed decisions.

Original languageEnglish (US)
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsRajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
PublisherAssociation for Computing Machinery
Pages1474-1477
Number of pages4
ISBN (Electronic)9781450321747
DOIs
StatePublished - Aug 11 2013
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F128815

Other

Other19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country/TerritoryUnited States
CityChicago
Period8/11/138/14/13

Keywords

  • Cancer
  • Disease detection
  • Disease surveillance
  • Epidemics
  • Inuenza
  • Public health
  • Social media
  • Twitter

ASJC Scopus subject areas

  • Software
  • Information Systems

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