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.
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. The authors analyze the selected communities within the anti-regime categories, focusing on their central actors, preferred online platforms, and activity surrounding real-world events. They use four variables to analyse the Syrian situation: 1). A direct collection of Twitter data associated with the conflict, leveraging existing authoritative sources; 2). A set of groups is generated from the collected data, achieved by community detection within a corresponding network representation; 3). A conceptual high-level categorization of the detected communities; 4). A detailed interpretation of selected communities, where the authors are particularly interested in demonstrating the complex nature of the conflict in contrast to other political environments.
This article will be of interest to researchers and practitioners interested in social network analysis in both the Syrian conflict but also in other complex political environments and conflicts. The methodology is beneficial to social network analysis by first identifying groups, their influencers and components and then tracking and assessing the changes in these groups over a select time period to identify changes in composition, beliefs, and interactions with other similar or antagonist groups. It is a process that can be applied to other multifaceted situations. The authors find that social media activity in Syria is considerably more convoluted than reported in other studies of online political activism, which suggests that alternative analytical approaches are necessary. The authors’ identification of communities and analysis of tweets, video and other content demonstrates the value of “small data” providing valuable research insights, while also complementing results found with analysis of larger data sets associated with conflict. Future research, according to the authors, will focus on these groups, with a view to monitoring the flux in group structure and ideology.