2014 BeatingtheNewswithEMBERSForecas

• (Ramakrishnan et al., 2014) ⇒ Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, Chang Tien Lu, Bert Huang, Aravind Srinivasan, Khoa Trinh, Lise Getoor, Graham Katz, Andy Doyle, Chris Ackermann, Ilya Zavorin, Jim Ford, Kristen Summers, Youssef Fayed, Jaime Arredondo, Dipak Gupta, and David Mares. (2014). “'Beating the News' with EMBERS: Forecasting Civil Unrest Using Open Source Indicators.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623373

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+%27Beating+the+News%27+with+EMBERS%3A+Forecasting+Civil+Unrest+Using+Open+Source+Indicators
• http://dl.acm.org/citation.cfm?id=2623330.2623373&preflayout=flat#citedby

Quotes

Author Keywords

• Civil unrest; data mining; event forecasting; open source indicators

Abstract

We describe the design, implementation, and evaluation of EMBERS, an automated, 24 x 7 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 BeatingtheNewswithEMBERSForecas	Naren Ramakrishnan Wei Wang Feng Chen Patrick Butler Nathan Self Sathappan Muthiah Rupinder Khandpur Parang Saraf Jose Cadena Anil Vullikanti Gizem Korkmaz Chris Kuhlman Achla Marathe Liang Zhao Ting Hua Chang Tien Lu Bert Huang Aravind Srinivasan Khoa Trinh Graham Katz Andy Doyle Chris Ackermann Ilya Zavorin Jim Ford Kristen Summers Youssef Fayed Jaime Arredondo Dipak Gupta David Mares Lise Getoor			'Beating the News' with EMBERS: Forecasting Civil Unrest Using Open Source Indicators				10.1145/2623330.2623373		2014