Social Networks

This 2013 book aims to inform government decision-makers, security analysts, and activists on how to use the social world to improve security locally, nationally, globally, and cost-effectively.

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This 2013 paper introduces a methodology that incorporates information available on terrorist networks, into the analysis of social networks underlying terrorist groups.

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This 2013 paper focuses on the modelling of terrorist networks online and addresses changes in technology and the associated creation of new security dynamics and threats.

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This 2014 report by the International Centre for the Study of Radicalization and Political Violence (ICSR) presents the findings of a 12-month study on the social media profiles of 190 Western and European foreign fighters.

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This paper discusses automated methods for detecting online radicalization and radical communities on various platforms, from websites, to forums, to social media platforms such as Facebook, YouTube, Twitter, and others.

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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 2013 article analyzes content generated on Twitter during the attacks at the Boston Marathon in April 2013. The authors perform an in-depth characterization of what factors influenced malicious content and profiles becoming viral.

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This 2010 article focuses on Social Network Analysis (SNA) and proposes a new generation of node centrality measures for weighted networks. The authors do this by building on previous research, which uses weights to operationalize tie strength between nodes within networks.

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This 2012 article focuses on Social Network Analysis (SNA) and particularly the challenge of identifying central nodes within a given social network. The authors propose a new centrality measure for weighted networks based on the amoeboid algorithm, which is called Physarum centrality.

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This 2014 article proposes an estimation method for relationship strength in weighted social network graphs. The authors argue that their method can fully estimate the relationship strength between any two users in a social network, whether they are directly connected or not.

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