Online Library

Terrorist groups are increasingly using encryption to plan and coordinate terrorist acts, leading to calls for the banning or backdooring of encrypted messaging apps. This would be misguided. It would not remove the ability of terrorist groups to access this technology and may push them to communicate in ways that are more difficult for Western intelligence agencies to monitor. By creating vulnerabilities in online tools used by a very large number of Americans and other users around the world, it would also expose the broader society to increased security risks.

International- and EU-institutions are paying increasing attention to the phenomenon of online hate speech and acknowledge this as a growing problem across and beyond Europe. In this regard, the 2015 ECRI report highlights online hate speech as one of the main trends of the previous year, emphasising that “hate speech through social media is rapidly increasing and has the potential to reach a much larger audience than extremist print media were able to reach previously”. Also, UNESCO has recently focused on this growing issue, mapping and analysing the existing initiatives to combat online hate speech in their comprehensive 2015 report “Countering online hate speech”.

Counter-narratives and campaigns promoting normality, are often highlighted as universal means against online propaganda from militant movements. However, such campaigns are driven by a number of unfortunate assumptions and are difficult to apply in practice.

Le Conseil national du numérique a été saisi le 25 juin 2014 de l’article 9 du projet de loi renforçant les
dispositions relatives à la lutte contre le terrorisme. Ces dispositions modifient l’article 6 de la loi du 21
juin 2004 pour la confiance dans l’économie numérique (LCEN) en prévoyant le blocage administratif des
sites diffusant des propos ou images provoquant à la commission d’actes terroristes ou en faisant
l’apologie. Elles élargissent également le champ des outils de notification imposés aux prestataires
techniques.
Afin de rendre un avis le plus éclairé possible dans le court délai imparti, le Conseil a procédé à une
quinzaine d’auditions, réunissant des experts du terrorisme (sociologues, journalistes, représentants
d’associations), de magistrats et avocats spécialisés, des représentants de la société civile, des
membres des services de renseignement et des professionnels du numérique (liste complète disponible
en annexe).

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone-wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone-wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups.

The ever-increasing use of the internet by terrorist groups as a platform for the dissemination of radical, violent ideologies is well documented. The internet has, in this way, become a breeding ground for potential lone-wolf terrorists; that is, individuals who commit acts of terror inspired by the ideological rhetoric emitted by terrorist organizations. These individuals are characterized by their lack of formal affiliation with terror organizations, making them difficult to intercept with traditional intelligence techniques. The radicalization of individuals on the internet poses a considerable threat to law enforcement and national security officials. This new medium of radicalization, however, also presents new opportunities for the interdiction of lone wolf terrorism. This dissertation is an account of the development and evaluation of an information technology (IT) framework for detecting potentially radicalized individuals on social media sites and Web fora. Unifying Collective Action Framing Theory (CAFT) and a radicalization model of lone wolf terrorism, this dissertation analyzes a corpus of propaganda documents produced by V several, radically different, terror organizations. This analysis provides the building blocks to define a knowledge model of terrorist ideological framing that is implemented as a Semantic Web Ontology. Using several techniques for ontology guided information extraction, the resultant ontology can be accurately processed from textual data sources. This dissertation subsequently defines several techniques that leverage the populated ontological representation for automatically identifying individuals who are potentially radicalized to one or more terrorist ideologies based on their postings on social media and other Web fora. The dissertation also discusses how the ontology can be queried using intuitive structured query languages to infer triggering events in the news. The prototype system is evaluated in the context of classification and is shown to provide state of the art results. The main outputs of this research are (1) an ontological model of terrorist ideologies (2) an information extraction framework capable of identifying and extracting terrorist ideologies from text, (3) a classification methodology for classifying Web content as resonating the ideology of one or more terrorist groups and (4) a methodology for rapidly identifying news content of relevance to one or more terrorist groups