‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators

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dc.contributor.authorRamakrishnan, Narenen
dc.contributor.authorButler, Patricken
dc.contributor.authorSelf, Nathanen
dc.contributor.authorKhandpur, Rupinder P.en
dc.contributor.authorSaraf, Parangen
dc.contributor.authorWang, Weien
dc.contributor.authorCadena, Joseen
dc.contributor.authorVullikanti, Anil Kumar S.en
dc.contributor.authorKorkmaz, Gizemen
dc.contributor.authorKuhlman, Christopher J.en
dc.contributor.authorMarathe, Achlaen
dc.contributor.authorZhao, Liangen
dc.contributor.authorTing, Huaen
dc.contributor.authorHuang, Berten
dc.contributor.authorSrinivasan, Aravinden
dc.contributor.authorTrinh, Khoaen
dc.contributor.authorGetoor, Liseen
dc.contributor.authorKatz, Grahamen
dc.contributor.authorDoyle, Andyen
dc.contributor.authorAckermann, Chrisen
dc.contributor.authorZavorin, Ilyaen
dc.contributor.authorFord, Jimen
dc.contributor.authorSummers, Kristenen
dc.contributor.authorFayed, Youssefen
dc.contributor.authorArredondo, Jaimeen
dc.contributor.authorGupta, Dipaken
dc.contributor.authorMares, Daviden
dc.contributor.authorMuthia, Sathappanen
dc.contributor.authorChen, Fengen
dc.contributor.authorLu, Chang-Tienen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2017-03-06T01:45:35Zen
dc.date.available2017-03-06T01:45:35Zen
dc.date.issued2014en
dc.description.abstractWe 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 uptick and downtick of incidents during the June 2013 protests in Brazil. 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.en
dc.description.notesdate-added: 2015-04-10 20:27:30 +0000 date-modified: 2016-03-12 22:32:58 +0000en
dc.format.extent1799 - 1808 page(s)en
dc.identifier.orcidHuang, B [0000-0002-8548-7246]en
dc.identifier.urihttp://hdl.handle.net/10919/75252en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.title‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicatorsen
dc.title.serialACM SIGKDD Conference on Knowledge Discovery and Data Miningen
dc.typeConference proceedingen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Instituteen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/Researchersen
pubs.organisational-group/Virginia Tech/University Research Institutes/Biocomplexity Institute/SelectedFaculty1en

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