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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.

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.

In recent decades, political parties espousing extreme nationalist, xenophobic, and even outright racist platforms have enjoyed variable success in national elections across Europe. While vibrant research literature has sought to better understand the sources of support for such parties, remarkably little attention has been paid to the interplay between parties and the broader social networks of extremism in which they are embedded. To remedy this deficiency, the present study examines the relations between far-right parliamentary parties and their extra-parliamentary networks. One level of analysis tests whether there is a relationship between a party’s position within a network and its sustainability. Social network analysis is employed to assess the nature and structure of ties between Belgian organizations online. In addition, systematic textual analysis of website content is used to determine how a party ideological position within the network impacts its sustainability. The second level of analysis is a qualitative study based on in-depth interviews with members of the Flemish nationalist organization in order to better understand how actors experience social networks. Evidence suggests that the most sustainable parties are those that have dense connections with other nationalist organizations. Mapping relations between far-right parties that compete openly within the rules of institutionalized democracy and their wider social networks can provide important policy-relevant insight into contemporary challenges posed by illiberal forces.

VOX-Pol Blog post.