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Safapp is a tool that crawls and analyses data from social media (specially Twitter and blogs) in the context of radicalization detection. This app has been developed in the context of SAFFRON European project. This paper focuses on the description of the semantic module of Safapp which is dedicated to the analysis of textual content of social networks and blogs, and more specifically on a newly developed module: the event extractor. With this module we expect to go one step further in the development of useful tools to track and analyze online propaganda.

Cet article porte sur les influenceurs, definis comme des individus qui parviennent a impacter la prise de decision d'autres individus en interagissant avec eux. Avec le succes des medias sociaux cette derniere decennie, la detection de tels profils engendre un interet croissant dans des domaines aussi varies que le marketing, la defense ou encore le journalisme. Nous presentons dans cet article une nouvelle approche a la detection des influenceurs par une modelisation empirique du processus d'influence. Nous definissons d'abord le cadre theorique dans lequel nous avons concu notre modele. Nous decrivons ensuite la methode employee pour determiner les composantes generales de notre modele et les caracteriser par des traits linguistiques a partir des messages d'un media social. Nous abordons enfin l'objectif d'implementer le modele avec la creation de donnees de reference.

Objective There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents. Materials and methods The collection of textual records in these hospitals was carried out between October 2009 and December 2010 in three French University hospitals (Lyon, Rouen and Nice). The following medical specialties were included in the study: digestive surgery, neurosurgery, orthopedic surgery, adult intensive-care units. Reference Standard surveillance was compared with the results of automatic detection using NLP. Sensitivity on 56 HAI cases and specificity on 57 non-HAI cases were calculated. Results The accuracy rate was 84% (n = 95/113). The overall sensitivity of automatic detection of HAIs was 83.9% (CI 95%: 71.7–92.4) and the specificity was 84.2% (CI 95%: 72.1–92.5). The sensitivity varies from one specialty to the other, from 69.2% (CI 95%: 38.6–90.9) for intensive care to 93.3% (CI 95%: 68.1–99.8) for orthopedic surgery. The manual review of classification errors showed that the most frequent cause was an inaccurate temporal labeling of medical events, which is an important factor for HAI detection. Conclusion This study confirmed the feasibility of using NLP for the HAI detection in hospital facilities. Automatic HAI detection algorithms could offer better surveillance standardization for hospital comparisons.

International audience; There has been increasing interest in the artificial intelligence community for influencer detection in recent years for its utility in singling out pertinent users within a large network of social media users. This could be useful, for example in commercial campaigns, to promote a product or a brand to a relevant target set of users. This task is performed either by analysing the graph-based representation of user interactions in a social network or by measuring the impact of the linguistic content of user messages in online discussions. We performed independent studies for each of these methods in the present paper with a hybridisation perspective. In the first study, we extract structural information to highlight influence among interaction networks. In the second, we identify linguistic features of influential behaviours. We then compute a score of user influence using centrality measures with the structural information for the former and a machine learning approach based on the relevant linguistic features for the latter.

Internet and Social media are widely used by terrorist organizations to spread their ideas and recruit foreign fighters. The aim of SAFFRON project 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 developed to support semantic analysis of social network. We focus on how SAFFRON makes use of natural language processing and machine learning to categorize and analyse messages dealing with recruitment and radicalization on social networks.