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Forecasting Social Unrest Using Activity Cascades

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dc.contributorVirginia Techen
dc.contributor.authorCadena, Joseen
dc.contributor.authorKorkmaz, Gizemen
dc.contributor.authorKuhlman, Christopher J.en
dc.contributor.authorMarathe, Achlaen
dc.contributor.authorRamakrishnan, Narenen
dc.contributor.authorVullikanti, Anilen
dc.date.accessioned2017-10-23T14:52:10Zen
dc.date.available2017-10-23T14:52:10Zen
dc.date.issued2015-06-19en
dc.description.abstractSocial unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.en
dc.description.sponsorshipThis work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center (Do(/NBC) contract number D12PC000337, National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM109718-01, Defense Threat Reduction Agency grant number HDTRA1-11-1-0016, Defense Threat Reduction Agency Comprehensive National Incident Management System Contract contract number HDTRA1-11-D-0016-0001, National Science Foundation (NSF) under grant number CCF-1216000 and NSF grant number CNS-1011769. The US Government is authorized to reproduce and distribute reprints of this work for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the US Government. None of the funders or grants represent commercial funding sources, since they are all US federal agencies.en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0128879en
dc.identifier.issue6en
dc.identifier.urihttp://hdl.handle.net/10919/79737en
dc.identifier.volume10en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleForecasting Social Unrest Using Activity Cascadesen
dc.title.serialPLOS Oneen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Scholarly Works, Fralin Life Sciences Institute