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Carbon: Forecasting Civil Unrest Events by Monitoring News and Social Media

  • Wei Kang
  • Jie Chen
  • Jiuyong Li
  • Jixue Liu
  • Lin Liu
  • Grant Osborne
  • Nick Lothian
  • Brenton Cooper
  • Terry Moschou
  • Grant Neale
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10604)

Abstract

Societal security has been receiving unprecedented attention over the past decade because of the ubiquity of online public data sources. Much research effort has been taken to detect relevant societal issues. However, forecasting them is more challenging but greatly beneficial to the entire society. In this paper, we present a forecasting system named Carbon to predict civil unrest events, e.g., protests and strikes. Two predictive models are implemented and scheduled to make predictions periodically. One model forecasts through the analysis of historical civil unrest events reported by news portals, while the other functions by detecting and integrating early clues from social media contents. With our web UI and visualisation, users can easily explore the predicted events and their spatiotemporal distribution. The demonstration will exemplify that Carbon can greatly benefit the society such that the general public can be alerted in advance to avoid potential dangers and that the authorities can take proactive actions to alleviate tensions and reduce possible damage to the society.

Keywords

Civil unrest Predictive models Open source data 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wei Kang
    • 1
  • Jie Chen
    • 1
  • Jiuyong Li
    • 1
  • Jixue Liu
    • 1
  • Lin Liu
    • 1
  • Grant Osborne
    • 2
  • Nick Lothian
    • 2
  • Brenton Cooper
    • 2
  • Terry Moschou
    • 2
  • Grant Neale
    • 2
  1. 1.University of South AustraliaMawson LakesAustralia
  2. 2.Data to Decisions CRCKent TownAustralia

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