Article

Planned protest modeling in news and social mediat

Authors:

Sathappan Muthiah,

Jaime Arredondo,

Naren RamakrishnanAuthors Info & Claims

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

Pages 3920 - 3927

Published: 25 January 2015 Publication History

Abstract

Civil unrest (protests, strikes, and "occupy" events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75% of the protests are planned, organized, and/or announced in advance; therefore detecting future time mentions in relevant news and social media is a direct way to develop a protest forecasting system. We develop such a system in this paper, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions. We illustrate the application of our system to 10 countries in Latin America, viz. Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant tradeoffs.

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Cited By

Basnet SSoh LSamal AJoshi D(2018)Analysis of Multifactorial Social Unrest Events with Spatio-Temporal k-Dimensional Tree-based DBSCANProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282870(1-10)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282866.3282870
Nasim MNguyen ALothian NCope RMitchell LChampin PGandon FMédini LLalmas MIpeirotis P(2018)Real-time Detection of Content Polluters in Partially Observable Twitter NetworksCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191574(1331-1339)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191574
Kamalloo ERafiei DChampin PGandon FMédini LLalmas MIpeirotis P(2018)A Coherent Unsupervised Model for Toponym ResolutionProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186027(1287-1296)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186027
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cover image Guide Proceedings

AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

January 2015

4331 pages

ISBN:0262511290

Sponsors

Association for the Advancement of Artificial Intelligence

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AAAI Press

Publication History

Published: 25 January 2015

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Cited By

Basnet SSoh LSamal AJoshi D(2018)Analysis of Multifactorial Social Unrest Events with Spatio-Temporal k-Dimensional Tree-based DBSCANProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282870(1-10)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282866.3282870
Nasim MNguyen ALothian NCope RMitchell LChampin PGandon FMédini LLalmas MIpeirotis P(2018)Real-time Detection of Content Polluters in Partially Observable Twitter NetworksCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191574(1331-1339)Online publication date: 23-Apr-2018
https://dl.acm.org/doi/10.1145/3184558.3191574
Kamalloo ERafiei DChampin PGandon FMédini LLalmas MIpeirotis P(2018)A Coherent Unsupervised Model for Toponym ResolutionProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186027(1287-1296)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186027
Khandpur RJi TJan SWang GLu CRamakrishnan NLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Crowdsourcing CybersecurityProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132866(1049-1057)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132866
Wang WNing YRangwala HRamakrishnan NMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting EventsProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983821(509-518)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983821
Muthiah SButler PKhandpur RSaraf PSelf NRozovskaya AZhao LCadena JLu CVullikanti AMarathe ASummers KKatz GDoyle AArredondo JGupta DMares DRamakrishnan NKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)EMBERS at 4 yearsProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939709(205-214)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939709
Agarwal SSureka AMarathe MMohania MMausam Jain P(2016)Investigating the Potential of Aggregated Tweets as Surrogate Data for Forecasting Civil ProtestsProceedings of the 3rd IKDD Conference on Data Science, 201610.1145/2888451.2888466(1-6)Online publication date: 13-Mar-2016
https://dl.acm.org/doi/10.1145/2888451.2888466
Hua TChen FZhao LLu CRamakrishnan N(2016)Automatic targeted-domain spatiotemporal event detection in twitterGeoinformatica10.1007/s10707-016-0263-020:4(765-795)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s10707-016-0263-0

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