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

Contextual Semantics for Radicalisation Detection on Twitter

Much research aims to detect online radical content mainly using radicalisation glossaries, i.e., by looking for terms and expressions associated with religion, war, offensive language, etc. However, such crude methods are highly inaccurate towards content that uses radicalisation terminology to simply report on current events, to share harmless religious rhetoric, or even to counter extremism. Language is complex and the context in which particular terms are used should not be disregarded. In this paper, we propose an approach for building a representation of the semantic context of the terms that are linked to radicalised rhetoric. We use this approach to analyse over 114K tweets that contain radicalisation-terms (around 17K posted by pro-ISIS users, and 97k posted by “general” Twitter users). We report on how the contextual information differs for the same radicalisation terms in the two datasets, which indicate that contextual semantics can help to better discriminate radical content from content that only uses radical terminology.The classifiers we built to test this hypothesis outperform those that disregard contextual informatio

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

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

Twitter influence and cumulative perceptions of extremist support: A case study of Geert Wilders

The advent of Social media has changed the manner in which perceptions about power and information can be influenced. Twitter is used as a fast‐paced vehicle to deliver short, succinct pieces of information, creating the perception of acceptance, popularity and authority. In the case of extremist groups, Twitter is one of several avenues to create the perception of endorsement of values that would otherwise gain less prominence through mainstream media. This study examines the use of Twitter in augmenting the status and reputation of anti‐Islam and anti‐immigration policy through the controlled release of social media information bursts. The paper demonstrates the use of new media by extremist groups using open source case study data from the associated Twitter traffic of Geert Wilders. The results indicate the pursuit of increased traction for controversial ideals that provoke and incite others towards extremism, violence, racism and Islamaphobia

Us against the World: Detection of Radical Language in Online Platforms

In this paper, we have investigated if we can detect radical comments in an online social network. We used comments from 6 subreddits, 3 of which are considered radical and 3 non-radical. Using various structural features of the texts in the comments, we were able to obtain an F1-score of 91% when using SVM with a linear kernel and a precision of almost 98% when using Random Forest

Analyzing the Growing Islamic Radicalization in France

Islamic radicalization in European countries is becoming more and more prevalent, as evidenced by the number of recent attacks by Muslims in Europe. I argue that the social, religious, and psychological environment in France creates a unique opportunity for Islamic radicalization, particularly through social media and in prisons. After defining radicalization and explaining two radicalization processes as well as different types of radicals, I analyze the specific factors present in France that contribute to this radicalization. I use case study analysis to examine several French citizens who radicalized, either online or in prison, in order to show how the recruiter exacerbated the situation in France. Additionally, I evaluate primary sources from the Islamic State and the Levant, in order to show how it capitalizes on certain aspects of French society, such as the discriminatory laws banning the burka. I also apply both theories of radicalization, and analyze which one matches the processes found in the case studies and primary sources. My findings support my hypothesis that France is a unique case where Islamic radicalization is more easily achieved, and that the presence of a mentor is crucial in the radicalization process