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Although social media have become important venues for right-wing populist (RWP) campaigns, the content, selection, and effects of RWP messages on social media remain largely unknown. Using content and panel analysis in two studies, we investigated the potential reciprocal relationship between RWP communication on social media and citizens’ anti-immigrant attitudes, anti-elitist attitudes, and feelings of anger and anxiety. In Study 1, we analyzed 13,358 Facebook posts from German and Austrian political parties and their leading candidates. Among our results, RWP actors conveyed anti-immigrant and anti-elitist messages more often than non-RWP actors, and anti-immigrant messages especially induced negative emotional responses among followers of RWP actors. In Study 2, our analysis of data from a two-wave panel study with 559 respondents revealed that anti-immigrant attitudes drove selective exposure to RWP content on Facebook, which consequently fueled anti-immigrant attitudes, and that selective exposure to such content increased individuals’ anti-elitist attitudes and anxiety.

This study explores the beliefs, narratives and ideologies that lead to violent radicalism underpinned by an abusive interpretation of Islam, with a view to understanding of the causes and remedies for violent radicalisation. The research was conducted through analysis and empirical data collection through 145 stakeholder and primary fieldwork interviews in four Member States; Denmark, France, Germany and the UK. The interview sample was intended to capture the diversity of the Muslim ‘field’ in each country as far as possible

In this article, we propose a general model of data collection from social media, in the context of terrorism research, focusing on recent studies of jihadists. By analyzing Twitter data collection methods in the existing research, we show that the methods used are prone to sampling biases, and that the sampled datasets are not sufficiently filtered or validated to ensure reliability of conclusions derived from them. Alternatively, we propose some best practices for the collection of data in future research on jihadist using social media (as well as other kinds of terrorist groups). Given the similarity of the methodological challenges posed by research on almost all social media platforms, in the context of terrorism studies, the critique and recommendations offered remain relevant despite the recent shift of most jihadists from Twitter to Telegram and other forms of social media.

This article presents the results of a study which measured the web presence of terrorist groups active in Sub Saharan Africa. It also explores the relationship between web technology availability and adoption by terrorist groups and looks at how differentiating between web publishing technologies used by terrorist groups can further develop the study of terrorist cultures and communities online.

Social media platforms have recently seen an increase in the occurrence of hate speech discourse which has led to calls for improved detection methods. Most of these rely on annotated data, keywords, and a classification technique. While this approach provides good coverage, it can fall short when dealing with new terms produced by online extremist communities which act as original sources of words which have alternate hate speech meanings. These code words (which can be both created and adopted words) are designed to evade automatic detection and often have benign meanings in regular discourse. As an example, “skypes”, “googles”, and “yahoos” are all instances of words which have an alternate meaning that can be used for hate speech. This overlap introduces additional challenges when relying on keywords for both the collection of data that is specific to hate speech, and downstream classification. In this work, we develop a community detection approach for finding extremist hate speech communities and collecting data from their members. We also develop a word embedding model that learns the alternate hate speech meaning of words and demonstrate the candidacy of our code words with several annotation experiments, designed to determine if it is possible to recognize a word as being used for hate speech without knowing its alternate meaning. We report an inter-annotator agreement rate of K=0.871, and K=0.676 for data drawn from our extremist community and the keyword approach respectively, supporting our claim that hate speech detection is a contextual task and does not depend on a fixed list of keywords. Our goal is to advance the domain by providing a high quality hate speech dataset in addition to learned code words that can be fed into existing classification approaches, thus improving the accuracy of automated detection.

Over the last four years as the Syrian uprising has grown into a full-blown civil war, a sinister parallel conflict has been fought out in cyberspace, with combatants wielding bytes and software rather than guns as they have battled for supremacy on Syria’s internet frontline.

But the consequences of this secret cyber war have been real and deadly – particularly for opponents of the Assad regime who have been targeted for arrest and torture as a consequence of personal information gleaned from their email traffic.

In some cases even the military plans of crucial rebel offensives had been hacked. But the opposition has been busy too, leaking President Bashar al-Assad’s embarrassing personal correspondence and eavesdropping on government troop deployments amid much else.

As a consequence Syria’s civil war has become fertile ground for ‘hacktivists’ from both sides – egged on and in some cases assisted by governments and agencies from outside the region.

In this special investigation for People & Power , Juliana Ruhfus has been finding out why some experts believe Syria’s electronic armies have been drawing up the blueprints for all wars of the future, conflicts that transcend traditional physical boundaries but which can be just as significant as those fought with tanks and missiles.