Understanding the Roots of Radicalisation on Twitter

In an increasingly digital world, identifying signs of online extremism sits at the top of the priority list for counter-extremist agencies. Researchers and governments are investing in the creation of advanced information technologies to identify and counter extremism through intelligent large-scale analysis of online data. However, to the best of our knowledge, these technologies are neither based on, nor do they take advantage of, the existing theories and studies of radicalisation. In this paper we propose a computational approach for detecting and predicting the radicalisation influence a user is exposed to, grounded on the notion of ’roots of radicalisation’ from social science models. This approach has been applied to analyse and compare the radicalisation level of 112 pro-ISIS vs.112 “general" Twitter users. Our results show the effectiveness of our proposed algorithms in detecting and predicting radicalisation influence, obtaining up to 0.9 F-1 measure for detection and between 0.7 and 0.8 precision for prediction. While this is an initial attempt towards the effective combination of social and computational perspectives, more work is needed to bridge these disciplines, and to build on their strengths to target the problem of online radicalisation

Artificial Intelligence and Online Extremism: Challenges and Opportunities

Radicalisation is a process that historically used to be triggered mainly through social interactions in places of worship, religious schools, prisons, meeting venues, etc. Today, this process is often initiated on the Internet, where radicalisation content is easily shared, and potential candidates are reached more easily, rapidly, and at an unprecedented scale (Edwards and Gribbon, 2013; Von Behr et al., 2013). In recent years, some terrorist organisations succeeded in leveraging the power of social media to recruit individuals to their cause and ideology (Farwell, 2014). It is often the case that such recruitment attempts are initiated on open social media platforms (e.g., Twitter, Facebook, Tumblr, YouTube) but then move onto private messages and/or encrypted platforms (e.g., WhatsApp, Telegram). Such encrypted communication channels have also been used by terrorist cells and networks to plan their operations (Gartenstein-Ross and Barr). To counteract the activities of such organisations, and to halt the spread of radicalisation content, some governments, social media platforms, and counter-extremism agencies are investing in the creation of advanced information technologies to identify and counter extremism through the development of Artificial Intelligent (AI) solutions (Correa and Sureka, 2013; Agarwal and Sureka 2015a; Scrivens and Davies, 2018). These solutions have three main objectives: (i) understanding the phenomena behind online extremism (the communication flow, the use of propaganda, the different stages of the radicalisation process, the variety of radicalisation channels, etc.), (ii) automatically detecting radical users and content, and (iii) predicting the adoption and spreading of extremist ideas. Despite current advancements in the area, multiple challenges still exist, including: (i) the lack of a common definition of prohibited radical and extremist internet activity, (ii) the lack of solid verification of the datasets collected to develop detection and prediction models, (iii) the lack of cooperation across research fields, since most of the developed technological solutions are neither based on, nor do they take advantage of, existing social theories and studies of radicalisation, (iv) the constant evolution of behaviours associated with online extremism in order to avoid being detected by the developed algorithms (changes in terminology, creation of new accounts, etc.) and, (v) the development of ethical guidelines and legislation to regulate the design and development of AI technology to counter radicalisation. In this book chapter we provide an overview of the current technological advancements towards addressing the problem of online extremism (with a particular focus on Jihadism). We identify some of the limitations of current technologies, and highlight some of the potential opportunities. Our aim is to reflect on the current state of the art and to stimulate discussions on the future design and development of AI technology to target the problem of online extremism

Effective Counterterrorism: What Have We Learned so Far?

The fight against terrorism, in particular of Islamist nature, has become a focus area of foreign and security policies in Western countries and around the world. This substantial effort is however only to a limited extent matched by adequate evaluations as to its actual success. This paper offers an overview of the counterterrorism effectiveness literature in terms of main areas of interest, conceptualisation and operationalisation difficulties as well as methodological considerations regarding the types of methods used, validity and reliability evaluations. It discusses the different understandings of causality and proposes a working definition of counterterrorism effectiveness. We find that a main focus of the literature lies on the impact component of effectiveness, often in the sense of a reduction of terrorist attacks in general or a reduction of certain methods of terrorism such as suicide attacks. Our model article "What Happened to Suicide Bombings in Israel? Insights from a Terror Stock Model" by Kaplan et al. (2005) illustrates the above-mentioned issues and reflects the mainstream approach in this field. The article uses econometric methods to determine the impact-effectiveness of counter-terrorism and reflects the problematique associated with attempts to infer a causal relationship between counterterrorism policies and the occurrence of terrorism.Counterterrorism, effectiveness, causality, quantitative and qualitative research methods

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

Reviewing Radicalization Research Using a Network Approach

In an effort to discern determinants of political radicalization, scholars have discussed and investigated a considerable number of personal or contextual constructs. Yet the existing literature reviews on this topic have mainly focused on specific data sources and research approaches (e.g., survey research), whereas an integrative overview is still missing. This study provides a systematic review of 57 published studies while particularly focusing on differences in the prevalence of considered determinants across research approaches (i.e., survey approaches, experimental approaches, and digital trace data approaches). As an innovative approach to systematic review, we apply a network approach for analyzing the most prevalent constructs and related hypotheses in the literature. Network analysis is particularly useful in this context because, it allows the visualization of the structure of constructs and hypotheses proposed in the field as well as the identification of crucial concepts. The review reveals differences across empirical approaches and closes with a discussion of over- and underresearched constructs, their generalizability across research approaches, and potentials for future research. We conclude by recommending a stronger integration of constructs and perspectives as well as a more rigid consideration of causal inference. Editorial Note: This article underwent a post-publication review and revision in response to criticism about problematic use of a closely related and previously published article. The corrected version was uploaded August 4, 2020.  Authors' Correction Note:Reviewing Radicalization Research Using a Network ApproachVeronika Batzdorfer & Holger Steinmetz In the corrected article, the authors respond to criticism regarding similarities in the literature search process and insufficient connections between a recent meta-analysis (Wolfowicz, Litmanovitz, Weisburd, & Hasisi, 2019) and the present paper. Although the present paper cited Wolfowicz et al. (2019) several times, these linkages were not presented well enough. In the corrected paper, these connections are emphasized in the following way:1) In the introduction, we note that the review builds on the meta-analysis by Wolfowicz et al. (2019) and stress the add-on value of our paper and the possibilities of fruitfully integrating both studies2) In the method section, we note the similarities of both reviews in the search process, data bases, and search terms3) In the discussion section, we added a discussion in which we integrate results of bothDue to the correction, readers are now better informed about similarities and differences of our studies.Wolfowicz, M., Litmanovitz, Y., Weisburd, D., & Hasisi, B. (2019). A field-wide systematic review and meta-analysis of putative risk and protective factors for radicalization outcomes. Journal of Quantitative Criminology, 1-41

TENSOR: retrieval and analysis of heterogeneous online content for terrorist activity recognition

The proliferation of terrorist generated content online is a cause for concern as it goes together with the rise of radicalisation and violent extremism. Law enforcement agencies (LEAs) need powerful platforms to help stem the influence of such content. This article showcases the TENSOR project which focusses on the early detection of online terrorist activities, radicalisation and recruitment. Operating under the H2020 Secure Societies Challenge, TENSOR aims to develop a terrorism intelligence platform for increasing the ability of LEAs to identify, gather and analyse terrorism-related online content. The mechanisms to tackle this challenge by bringing together LEAs, industry, research, and legal experts are presented

Unsupervised and Language Independent Approach to Extremism and Collective Radicalization Understanding

Increasingly in social media, we find cases where groups are organized to protest against something, often in those groups, members with extremist ideologies are inserted. These cases are happing more often, groups are created for the organization of peaceful protests and someone starts a topic with an extremist language leading, sometimes, to a radicalisation of the group. This research aims to create an approach that allows the detection of cases of extremism and collective radicalisation within social networks, this should be done in an unsupervised and independent of language way. The methods used to achieve the intended objectives are the creation of a lexicon of extreme sentiment terms named ExtremeSentiLex and a classifier of extreme sentiment in which the input is the extreme sentiment terms and the social network post. For the development of these tools were used purely statistical natural language processing methods. To validate the ExtremeSentiLex it was applied using the extreme sentiment classifier, the input posts that are analysed are posts from a dataset already validated by the scientific community. For a comparative study, word embeddings are used to expand the first ExtremeSentiLex obtained and a test is also performed in which the ExtremeSentiLex is balanced and applied to a balanced polarity dataset. The results obtained in this content level research that will be available to the scientific community are the ExtremeSentiLex and several datasets that were evaluated by us regarding the presence of extreme sentiment. At the level of tests performed when the ExtremeSentiLex was validated, the level of precision in finding extreme sentiment at the correct polarity was very high. When applying word embeddings the results dropped. Regarding the ExtremeSentiLex and balanced dataset, the results were very positive. It has been concluded that our dataset is suitable for the application in detecting extreme sentiments in text. Furthermore, it was found that with the help of linguistic and psychological experts the ExtremeSentiLex could be improved. However, this investigation aimed to do so using purely statistical methods. This goal has been successfully achieved.Cada vez mais nos social medias encontramos grupos que se organizam para protestarem contra algo e, muitas vezes, nesses mesmos grupos por vezes estão inseridos membros com ideologias extremistas, com o intuito de destabilizar a ordem publica e espalhar os seus ideias recorrendo ao terror. Verifica-se que estes casos são cada vez mais recorrentes, ao criar-se um grupo específico cuja finalidade é a realização de protestos pacíficos com objetivos liberais e concretos, existe muitas vezes alguém que inicia um tópico com linguagem extremista. E, daqui, justificado pela influência de grupo, é possível ter-se em consideração a possibilidade de radicalização coletiva. O objetivo desta investigação é criar uma abordagem para deteção de casos de extremismo e radicalização coletiva em redes sociais e isto deve ser feito de forma não supervisionada e independente da língua. Os métodos utilizados foram: a criação de um léxico de termos de sentimento extremo denominado ExtremeSentiLex e de um classificador de sentimentos extremos em que o input são os termos de sentimento extremo e os posts de redes sociais. Para o desenvolvimento destas ferramentas foram utilizados métodos de processamento da linguagem natural puramente estatísticos. Sendo que, para podermos validar o ExtremeSentiLex este foi aplicado recorrendo ao classificador de sentimentos extremos e aos posts de input que são analisados que são posts de datasets já validados pela comunidade cientifica. Para um estudo comparativo, são utilizados word embeddings para expandir o ExtremeSentiLex obtido e é também feito um teste em que o ExtremeSentiLex é balanceado e aplicado a um dataset também balanceado a nível da polaridade de sentimentos. Os resultados obtidos nesta investigação e que serão disponibilizados para a comunidade cientifica são: o ExtremeSentiLex e datasets, que foram avaliados, relativamente à presença de sentimentos extremos; Os testes efetuados aquando da validação do ExtremeSentiLex: o nível de precisão ao encontrar sentimentos extremos na polaridade correta foi muito elevada. Já aquando da aplicação dos word embeddings os resultados pioraram; Com ExtremeSentiLex e dataset balanceados, os resultados melhoraram. Concluí-se que o ExtremeSentiLex é adequado para a deteção de sentimentos extremos em texto. Detetou-se ainda que com a ajuda de especialistas na área da linguística e da psicologia o ExtremeSentiLex poderia ser aprimorado. Contudo o objetivo desta investigação era apenas fazê-lo recorrendo a métodos puramente estatísticos

“Follow Me So I Can DM You Back”: An Exploratory Analysis of a Female Pro- ISIS Twitter Network

The purpose of this study is to explore a network of female pro-Islamic State of Syria and Iraq (ISIS) supporters on Twitter. To do so, I identified twenty Twitter accounts (n=20) through snowball sampling, and analyzed their network comprising 5,861 vertices and 12,034 edges. I studied the network using three social network analysis metrics—Freeman’s normalized betweenness centrality, average geodesic distance, and tie strength. Females in the sample were more influential than males, and as a result, had a greater ability to radicalize other females within their network. Further, I observed that it took females longer than expected to send information within the network, according to the Three Degrees of Influence Theory. Finally, I found that most ties within the network were not reciprocated. In line with the Strength of Weak Ties Theory, Pro-ISIS females have a unique ability to radicalize others to support pro-jihadist terrorism on Twitter. I conclude that despite the long average geodesic distance, certain pro-ISIS females can successfully encourage other women to radicalize. Public safety officials, Twitter, and other researchers must respond to this phenomenon accordingly

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page