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

• Published in: Advanced Data Mining and Applications Vol. 10604; pp. 859 - 865
• Main Authors: Kang, Wei, Chen, Jie, Li, Jiuyong, Liu, Jixue, Liu, Lin, Osborne, Grant, Lothian, Nick, Cooper, Brenton, Moschou, Terry, Neale, Grant
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Civil unrest; Open source data; Predictive models
• ISBN: 9783319691787; 3319691783
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-69179-4_62

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.