research-article

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

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1799 - 1808

https://doi.org/10.1145/2623330.2623373

Published: 24 August 2014 Publication History

Abstract

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.

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https://doi.org/10.26599/TST.2024.9010067
Rheault ENerayo MLeonard JKolenbrander JHenshaw CBoswell MMichaels A(2024)Use and Abuse of Personal Information, Part I: Design of a Scalable OSINT Collection EngineJournal of Cybersecurity and Privacy10.3390/jcp40300274:3(572-593)Online publication date: 13-Aug-2024
https://doi.org/10.3390/jcp4030027
Hamdani JAntuña Molina PLeva Fuentes LShawqy HRossi GAndrés León D(2024)What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban SquareUrban Planning10.17645/up.855110Online publication date: 31-Oct-2024
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Index Terms

'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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IARPA via DoI/NBC

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Liu XHe YTai WXu XZhou FLuo G(2025)Exploring the Chameleon Effect of Contextual Dynamics in Temporal Knowledge Graph for Event PredictionTsinghua Science and Technology10.26599/TST.2024.901006730:1(433-455)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2024.9010067
Rheault ENerayo MLeonard JKolenbrander JHenshaw CBoswell MMichaels A(2024)Use and Abuse of Personal Information, Part I: Design of a Scalable OSINT Collection EngineJournal of Cybersecurity and Privacy10.3390/jcp40300274:3(572-593)Online publication date: 13-Aug-2024
https://doi.org/10.3390/jcp4030027
Hamdani JAntuña Molina PLeva Fuentes LShawqy HRossi GAndrés León D(2024)What Is My Plaza for? Implementing a Machine Learning Strategy for Public Events Prediction in the Urban SquareUrban Planning10.17645/up.855110Online publication date: 31-Oct-2024
https://doi.org/10.17645/up.8551
Deng Sde Rijke MNing YBaeza-Yates RBonchi F(2024)Advances in Human Event Modeling: From Graph Neural Networks to Language ModelsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671466(6459-6469)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671466
Singh PJain A(2024)Context-Aware Civil Unrest Event Prediction Using Neutrosophic-Aspect-Based Sentiment Analysis, PSO, and Hierarchical LSTMIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333850911:3(3667-3677)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3338509
Ozer AWaggoner PKennedy R(2024)The Paradox of Algorithms and Blame on Public Decision-makersBusiness and Politics10.1017/bap.2023.35(1-18)Online publication date: 18-Mar-2024
https://doi.org/10.1017/bap.2023.35
Xu SLiu JLi SYang SLi F(2023)Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A SurveyApplied Sciences10.3390/app13241334613:24(13346)Online publication date: 18-Dec-2023
https://doi.org/10.3390/app132413346
Yinsen WXin ZYan PZexin F(2023)A Temporal Attention-based Model for Social Event Prediction2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191427(1-8)Online publication date: 18-Jun-2023
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Bewong MWondoh JKwashie SLiu JLiu LLi JIslam MKernot D(2023)DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long TextIEEE Access10.1109/ACCESS.2023.326265311(32826-32841)Online publication date: 2023
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Chu SLiu XMarathe ADeng X(2023)A latent process approach to change-point detection of mixed-type observationsQuality Engineering10.1080/08982112.2023.222361736:2(407-426)Online publication date: 28-Jun-2023
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