'Beating the news' with EMBERS: forecasting civil unrest using open source indicators


We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

DOI: 10.1145/2623330.2623373
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@inproceedings{Ramakrishnan2014BeatingTN, title={'Beating the news' with EMBERS: forecasting civil unrest using open source indicators}, author={Naren Ramakrishnan and Patrick Butler and Sathappan Muthiah and Nathan Self and Rupinder Paul Khandpur and Parang Saraf and Wei Wang and Jose Cadena and Anil Vullikanti and Gizem Korkmaz and Chris J. Kuhlman and Achla Marathe and Liang Zhao and Ting Hua and Feng Chen and Chang-Tien Lu and Bert Huang and Aravind Srinivasan and Khoa Trinh and Lise Getoor and Graham Katz and Andy Doyle and Chris Ackermann and Ilya Zavorin and Jim Ford and Kristen Maria Summers and Youssef Fayed and Jaime Arredondo and Dipak K Gupta and David Mares}, booktitle={KDD}, year={2014} }