Datasets

In 2013, The SecDev Group undertook a 10-month Kanishka-funded project that set out, in part,  to explore methodologies and technologies for open source social media (OSSM) research and their potential utility for detecting weak signals of radicalization towards violent extremism online.

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This research note summarizes experimental research conducted by The SecDev Group in 2013, as part of a Public Safety Canada, Kanishka-funded project looking at social media analytics and the prevention of violent extremism.

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This experiment used the Twitter profile of a U.K. national, verified to be an active foreign fighter (FF) in the Syrian civil war, as the seed for constructing a network topology based on social media interactions.

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This summary captures the main findings of a longitudinal content analysis of a known foreign fighter’s (FF) public social media activity for signs of radicalization toward violent extremism.

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This 2013 article focuses on identifying and measuring the influence of extremist accounts on Twitter, assessing extremist content, and determining ways to counter violent extremism (CVE) in online social networks.

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This 2011 book uses a survey created by the authors to examine the growth of populist, also known as “nationalist” or “far-right”, parties in Europe by looking at their overall social media presence and analyzing the responses of online supporters.

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This paper explores the use of crawling global social networking platforms to undercover previously unknown radicalized individuals. To prove the utility of this process the authors collect a YouTube dataset from a group that potentially has a radicalizing agenda.

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This 2014 article focuses on Social Network Analysis (SNA) and in particular content propagation and dissemination on Facebook.

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This 2014 paper utilizes a form of network representation that incorporates multiple data views to uncover and categorize networks of accounts that fall into four broadly defined groups in the Syrian conflict.

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This article studied a selection of right-wing extremist (RWE) groups on Twitter. The authors looked at particular language-based networks as case studies, collecting Twitter data for groups across eight countries.

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