Inferring causal networks behind observed data is an active area of research with wide applicability to areas such as epidemiology, microbiology and social science. In particular recent research has focused on identifying how information propagates through the Internet. This research has so far only used temporal features of observations, and while reasonable results have been achieved, there is often further information which can be used.

We present NOAM, an integrated platform for the monitoring and analysis of news media content. NOAM is the data management system behind various applications and scientific studies aiming at modelling the mediasphere. The system is also intended to address the need in the AI community for platforms where various AI technologies are integrated and deployed in the real world. It combines a relational database (DB) with state of the art AI technologies, including data mining, machine learning and natural language processing. These technologies are organised in a robust, distributed architecture of collaborating modules, that are used to populate and annotate the DB. NOAM manages tens of millions of news items in multiple languages, automatically annotating them in order to enable queries based on their semantic properties. The system also includes a unified user interface for interacting with its various modules.