Context matters

Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data

Pedro Manrique, Hong Qi, Ana Morgenstern, Nicolas Velasquez, Tsai Ching Lu, Neil F Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.

Original languageEnglish (US)
Title of host publicationIEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics
Pages169-172
Number of pages4
DOIs
StatePublished - 2013
Event11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013 - Seattle, WA, United States
Duration: Jun 4 2013Jun 7 2013

Other

Other11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013
CountryUnited States
CitySeattle, WA
Period6/4/136/7/13

Fingerprint

Moderators
Big data

Keywords

  • big data
  • civil unrest
  • emerging phenomena
  • open source indicator
  • prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Manrique, P., Qi, H., Morgenstern, A., Velasquez, N., Lu, T. C., & Johnson, N. F. (2013). Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data. In IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics (pp. 169-172). [6578812] https://doi.org/10.1109/ISI.2013.6578812

Context matters : Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data. / Manrique, Pedro; Qi, Hong; Morgenstern, Ana; Velasquez, Nicolas; Lu, Tsai Ching; Johnson, Neil F.

IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. p. 169-172 6578812.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Manrique, P, Qi, H, Morgenstern, A, Velasquez, N, Lu, TC & Johnson, NF 2013, Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data. in IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics., 6578812, pp. 169-172, 11th IEEE International Conference on Intelligence and Security Informatics, IEEE ISI 2013, Seattle, WA, United States, 6/4/13. https://doi.org/10.1109/ISI.2013.6578812
Manrique P, Qi H, Morgenstern A, Velasquez N, Lu TC, Johnson NF. Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data. In IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. p. 169-172. 6578812 https://doi.org/10.1109/ISI.2013.6578812
Manrique, Pedro ; Qi, Hong ; Morgenstern, Ana ; Velasquez, Nicolas ; Lu, Tsai Ching ; Johnson, Neil F. / Context matters : Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data. IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics. 2013. pp. 169-172
@inproceedings{4b07dabf49ea4d42aa0755b8803553e3,
title = "Context matters: Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data",
abstract = "Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.",
keywords = "big data, civil unrest, emerging phenomena, open source indicator, prediction",
author = "Pedro Manrique and Hong Qi and Ana Morgenstern and Nicolas Velasquez and Lu, {Tsai Ching} and Johnson, {Neil F}",
year = "2013",
doi = "10.1109/ISI.2013.6578812",
language = "English (US)",
isbn = "9781467362115",
pages = "169--172",
booktitle = "IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics",

}

TY - GEN

T1 - Context matters

T2 - Improving the uses of big data for forecasting civil unrest: Emerging phenomena and big data

AU - Manrique, Pedro

AU - Qi, Hong

AU - Morgenstern, Ana

AU - Velasquez, Nicolas

AU - Lu, Tsai Ching

AU - Johnson, Neil F

PY - 2013

Y1 - 2013

N2 - Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.

AB - Open Source Indicators (OSI) such as Google Trends (GT) promise to uncover the social dynamics associated with behavior that precede episodes of civil unrest. There are myriad reasons why societies may become unstable: Our analysis does not require or inquire the underlying reasons for discontent but instead takes into account differences associated with variegated social contexts. This paper examines instances of this volatile behavior and suggests a simple model for predicting civil unrest events using GT as an open source indicator (OSI). It grounds the possibilities for prediction on the fact that social processes occur within a particular social context. As such, paying attention to the particular signals associated from each country is an important moderator for any model keen on predicting cases of extreme social behavior such as civil unrest.

KW - big data

KW - civil unrest

KW - emerging phenomena

KW - open source indicator

KW - prediction

UR - http://www.scopus.com/inward/record.url?scp=84883416842&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883416842&partnerID=8YFLogxK

U2 - 10.1109/ISI.2013.6578812

DO - 10.1109/ISI.2013.6578812

M3 - Conference contribution

SN - 9781467362115

SP - 169

EP - 172

BT - IEEE ISI 2013 - 2013 IEEE International Conference on Intelligence and Security Informatics: Big Data, Emergent Threats, and Decision-Making in Security Informatics

ER -