Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Conditional random fields for entity extraction and ontological text coding
Computational & Mathematical Organization Theory
Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business
Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Near real time assessment of social media using geo-temporal network analytics
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In the face of uprisings and revolutions happening in several countries within short period of time (Arab Spring 2011), the need for fast network assessments is compelling. In this article we present a rapid network assessment approach which uses a vast amount of pre-indexed news data to provide up-to-date overview and orientation in emerging and ongoing incidents. We describe the fully automated process of preparing the data and creating the dynamic meta-networks. We also describe the network analytical measures that we are using to identify important topics, persons, organizations, and locations in these networks. With our rapid network modeling and analysis approach first results can be provided within hours. In the explorative study of this article we use 108,000+ articles from 600+ English written news sources discussing Egypt, Libya, and Sudan within a time period of 18 months to show an application scenario of our approach. In particular we are looking at the involvement of other countries and their politicians during time periods of major incidents.