Detecting and tracking disease outbreaks by mining social media data

Yusheng Xie, Zhengzhang Chen, Yu Cheng, Kunpeng Zhang, Ankit Agrawal, Wei-Keng Liao, Alok Nidhi Choudhary

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

15 Scopus citations

Abstract

The emergence and ubiquity of online social networks have enriched web data with evolving interactions and communities both at mega-scale and in real-time. This data offers an unprecedented opportunity for studying the interaction between society and disease outbreaks. The challenge we describe in this data paper is how to extract and leverage epidemic outbreak insights from massive amounts of social media data and how this exercise can benefit medical professionals, patients, and policymakers alike. We attempt to prepare the research community for this challenge with four datasets. Publishing the four datasets will commoditize the data infrastructure to allow a higher and more efficient focal point for the research community.

Original languageEnglish (US)
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Pages2958-2960
Number of pages3
StatePublished - Dec 1 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: Aug 3 2013Aug 9 2013

Other

Other23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
CountryChina
CityBeijing
Period8/3/138/9/13

ASJC Scopus subject areas

  • Artificial Intelligence

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