Much attention has been given to the impact of informativeness and similarity measures on distributional thesauri. We investigate the effects of context filters on thesaurus quality and propose the use of cooccurrence frequency as a simple and inexpensive criterion. For evaluation, we measure thesaurus agreement with WordNet and performance in answering TOEFL-like questions. Results illustrate the sensitivity of distributional thesauri to filters.

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Internet and Social media are widely used by terrorist organizations to spread their ideas and recruit foreign fighters. The aim of SAFFRON is to build a system able to support early detection of foreign fighters' recruitment by terrorist groups in Europe. It consists in studying recruitment communication strategies on social media (e.g. narrations, argumentative tropes and myths used), and their evolution in time, as well as in identifying needs, values, cultural and social contexts of the target groups (young foreign fighters). In this paper, we will describe SafApp, the application we have developed to support semantic analysis of social network with a particular focus on how SAFFRON makes use of natural language processing and machine learning to categorize and analyse content of messages dealing with recruitment and radicalization on social networks.