Online Library

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 radicalisationterms
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 information.

This study is a content analysis of Islamic State of Iraq and Syria (ISIS)’s French-language magazine Dar al-Islam. The first seven issues of the magazine are quantitatively examined and broken down into the number of articles, images, and terms used as a means of determining how ISIS targets French-speaking individuals. This study find that ISIS focuses on religious terminology and justifications to rationalize its existence and its fight. Also, despite being a French-language magazine, a majority of the focus is on Middle Eastern groups, not Western groups. Overall, the magazine is similar, but not a carbon copy to ISIS’s English-language magazine, Dabiq.

A letter to Mr. Zuckerberg from the UN Rapporteur on the definition of terrorism.

This article aims to address two questions: how does hate speech manifest on North American white supremacist websites; and is there a connection between online hate speech and hate crime? Firstly, hate speech is defined and the research methodology upon which the article is based is explained. The ways that ‘hate’ groups utilize the Internet and their purposes in doing so are then analysed, with the content and the functions of their websites as well as their agenda examined. Finally, the article explores the connection between hate speech and hate crime. I argue that there is sufficient evidence to suggest that speech can and does inspire crime. The article is based in the main on primary sources: a study of many ‘hate’ websites; and interviews and discussions with experts in the field.

From March 8 to June 8, 2018, the Counter Extremism Project (CEP) conducted a study to better understand how ISIS content is being uploaded to YouTube, how long it is staying online, and how many views these videos receive. To accomplish this, CEP conducted a limited search for a small set of just 229 previously-identified ISIS terror-related videos from among the trove of extremist material available on the platform. CEP used two computer programs to locate these ISIS videos: a web crawler to search video titles and descriptions for keywords in videos uploaded to YouTube, and eGLYPH, a robust hashing content-identification system. CEP’s search of a limited set of ISIS terror-related videos found that hundreds of ISIS videos are uploaded to YouTube every month, which in turn garner thousands of views.

Online extremists’ use of social media poses a new form of threat to the general public. These extremists range from cyberbullies to terrorist organizations. Social media providers often suspend the extremists’ accounts in response to user complaints. However, extremist users can simply create new accounts and continue their activities. In this work we present a new set of operational capabilities to address the threat posed by online extremists in social networks. We use thousands of Twitter accounts related to the Islamic State in Iraq and Syria (ISIS) to develop behavioral models for these users—in particular, what their accounts look like and with whom they connect. We use these models to track existing extremist users by identifying pairs of accounts belonging to the same user. We then present a model for efficiently searching the social network to find suspended users’ new accounts based on a variant of the classic Pólya’s urn setup. We find a simple characterization of the optimal search policy for this model under fairly general conditions. Our urn model and main theoretical results generalize easily to search problems in other fields.