Discovering word senses from a network of lexical cooccurrences
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Information Retrieval: Implementing and Evaluating Search Engines
Information Retrieval: Implementing and Evaluating Search Engines
Digging for knowledge with information extraction: a case study on human gene-disease associations
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
Foundations of Multidimensional Network Analysis
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Lessons learned in using social media for disaster relief - ASU crisis response game
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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We develop a framework that uses Web content to obtain quantitative information about a phenomenon that would otherwise require the operation of large scale, expensive intelligence exercises. Exploiting indexed reliable sources such as online newspapers and blogs, we use unambiguous query terms to characterize a complex evolving phenomena and solve a security policy problem: identifying the areas of operation and modus operandi of criminal organizations, in particular, Mexican drug trafficking organizations over the last two decades. We validate our methodology by comparing information that is known with certainty with the one we extracted using our framework. We show that our framework is able to use information available on the web to efficiently extract implicit knowledge about criminal organizations. In the scenario of Mexican drug trafficking, our findings provide evidence that criminal organizations are more strategic and operate in more differentiated ways than current academic literature thought.