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Social media channels, such as Facebook or Twitter, allow for people to express their views and opinions about any public topics. Public sentiment related to future events, such as demonstrations or parades, indicate public attitude and therefore may be applied while trying to estimate the level of disruption and disorder during such events. Consequently, sentiment analysis of social media content may be of interest for different organisations, especially in security and law enforcement sectors. This paper presents a new lexicon-based sentiment analysis algorithm that has been designed with the main focus on real time Twitter content analysis. The algorithm consists of two key components, namely sentiment normalisation and evidence-based combination function, which have been used in order to estimate the intensity of the sentiment rather than positive/negative label and to support the mixed sentiment classification process. Finally, we illustrate a case study examining the relation between negative sentiment of twitter posts related to English Defence League and the level of disorder during the organisation’s related events.

Many cases of radicalism, especially violent radicalism, manifest within the context of inter-ethnic conflict and war. Research in this domain has significantly contributed to our overarching comprehension of the issue. This form of radicalism is inherently linked to the dynamics of group affiliation. However, our focus here is directed towards the individual motivation driving radical actions. While radical actions may encompass dynamics of group affiliation (e.g., the most radical pro-life activists being members of the Army of God), they also possess a substantial individual component. For instance, numerous attacks on abortion doctors or clinics are categorized as ’lone-wolf terrorism’.

In this study, we sought to examine whether state-of-the-art Machine Learning tech- nology for text analysis could effectively discern the intent behind radical actions from user-generated content. We analyzed a diverse array of radical texts published online by various activists, including anarchists, Iranian guerrilla members, Indian revolution- aries, activists from the US civil rights movement, and The Army of God. Our analysis reveals that Machine Learning models can successfully identify potential signals indica- tive of the author’s intent behind radical actions.

This chapter is a contribution to the debate on information resilience and national security from the perspective of countering violent Islamist extremism in the arc of instability. The narratives crafted by various violent Islamist extremists are a form of information resilience, but in this case, they act as counterforces, hindering state efforts to prepare for and withstand the onslaughts of terror groups. This challenge is particularly pronounced in weak and fragile states within the arc of instability. The aims of the chapter are threefold: to explore the information resilience of terrorist groups vying for power in the arc of instability; to examine the reactions and responses that extremism has produced in the commercial media; and, finally, to discuss the key challenges that governments are grappling with in controlling media content that is seen as having the potential to destabilise the already fragile security situation.

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”.

Within the short timeframe of a few months, a new terrorist group managed to solidify its presence in the Middle East in order to begin radicalizing and recruiting foreigners to fight jihad and declare an overarching goal of creating a purely Islamic State. DAESH quickly established their brand and a media strategy and became known worldwide for the high production value of their communication operations just as much as for their savage cruelty. The use of media has become vital to its overall success as a terrorist organization. Through targeted propaganda strategies, DAESH has managed to appeal to marginalized foreigners. While the populations of the Western world, largely opposed to DAESH, receive videos of beheadings and barbarity, potential recruits are inundated with images of state-building, charity work, and brotherhood. Concurrently, DAESH has managed to utilize the mainstream media landscape in order to further facilitate anti- Islamic rhetoric and intensify the air of mystery surrounding the organization. Through the sophisticated use of technology, as well as a detailed understanding and exploitation of human behavior and psychology, DAESH has managed to establish themselves as one of the most adept terror organizations, due in large part to the successful intersection of their communication operations with targeted propaganda strategies.

Crafted as an open communication platform characterized by high anonymity and minimal moderation, Telegram has garnered increasing popularity among activists operating within repressive political contexts, as well as among political extremists and conspiracy theorists. While Telegram offers valuable data access to research non-institutionalized activism, scholars studying the latter on Telegram face unique theoretical and methodological challenges in systematically defining, selecting, sampling, and classifying relevant actors and content. This literature review addresses these issues by considering a wide range of recent research. In particular, it discusses the methodological challenges of sampling and classifying heterogeneous groups of (often non-institutionalized) actors. Drawing on social movement research, we first identify challenges specific to the characteristics of non-institutionalized actors and how they become interlaced with Telegram’s platform infrastructure and requirements. We then discuss strategies from previous Telegram research for the identification and sampling of a study population through multistage sampling procedures and the classification of actors. Finally, we derive challenges and potential strategies for future research and discuss ethical challenges.