Automatically Detecting the Resonance of Terrorist Movement Frames on the Web

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

Mapping extremist forums using text mining

Political opinions far from what is considered normal, a distorted view of reality, and hatred to certain other groups are spread amongst political extremists like Islamists and White Supremacists. Demonstrations and violence performed by some members of these groups are well-known from mass media and get a lot of attention. The Islamists and right-extremists exploit this benefit to spread their message to ordinary people. In online forums, young, curious people can read detailed information (or propaganda) from extremists. Which words do extremists then use to convince each other in addition to other curious readers that what they stand for is right? The goal of this thesis is to first find algorithms or techniques for how to discover characteristic vocabulary in online extremist forums and words that frequently are used in the same forum message. Then we analyse the results to find patterns of what is typical vocabulary in the different forums. Mapping the extremists’ habits of vocabulary usage can help us know better how extremists write in online extremist forums, and possibly also help us recognize them when they write on some other websites. In this thesis, we find frequent and characteristic words by means of Global Term Frequency (GTF) and pairs of co-occurring words by means of odds ratio in different extremist forums. We compare normalized GTF (NGTF) of words in two forums to find out where they are used most. Words used in only one of two forums are found as well. We find the GTFs for words written by five of the ten most active authors in each forum, and we find words that one author writes, while the other of ten most active authors does not write. From results we see that Islamists write most about religion, but also some politics. Some popular words are “allah”, “prophet”, “fasting”, and “hajj”. The right-extreme websites Stormfront and Vigrid discuss politics and argument for their own ideology and against the mainstream politics. Frequent words in Stormfront are “white”, “jews”, and “race”, in the Norwegian Vigrid website “jødene”, “tyskland”, and “krigen”. In the German right-extreme website Deutsche Stimme, “npd”, “Deutschland”, “partei”, and “volk” are frequent words. Both Islamists and right-extremists are preoccupied by family values. Our results are useful for discovering topics that extremists write about in their online forums, topics that other people do not write about at all or write about with a different point of view