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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.

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”.

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.

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.

Acts of terror lead to both a rise of an extended sense of fear that goes beyond the physical location of the attacks and to increased expressions of online hate. In this longitudinal study, we analyzed dynamics between the exposure to online hate and the fear of terrorism after the Paris attacks in November 13, 2015. We hypothesized that exposure to online hate is connected to a perceived Zeitgeist of fear (i.e., collective fear). In turn, the perceived Zeitgeist of fear is related to higher personal fear of terrorism both immediately after the attacks and a year later. Hypotheses were tested using path modeling and panel data (N = 2325) from Norway, Finland, Spain, France, and the United States a few weeks after the Paris attacks in November 2015 and again a year later in January 2017. With the exception of Norway, exposure to online hate had a positive association with the perceived Zeitgeist of fear in all our samples. The Zeitgeist of fear was correlated with higher personal fear of terrorism immediately after the attacks and one year later. We conclude that online hate content can contribute to the extended sense of fear after the terrorist attacks by skewing perceptions of social climate.