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In this study, we select the official websites of four known extremist groups and map the networks of hyperlinked websites forming a virtual community around them. The networks are constructed using a custom-built webcrawler (TENE: Terrorism and Extremism Network Extractor) that searches the HTML of a website for all the hyperlinks inserted directing to other websites (Bouchard et al., 2014). Following all of these hyperlinks out of the initial website of interest produces the network of websites forming a community that is more or less cohesive, more or less extensive, and more or less devoted to the same cause (Bouchard and Westlake, 2016; Westlake and Bouchard, 2016). The extent to which the official website of a group contains many hyperlinks towards external websites may be an indicator of a more active community, and it may be indicative of a more active social movement.

The purpose of this paper is to discuss the design, application and findings of a case study in which the application of a machine learning algorithm is utilised to identify the grievances in Twitter in an Arabian context. To understand the characteristics of the Twitter users who expressed the identified grievances, data mining techniques and social network analysis were utilised. The study extracted a total of 23,363 tweets and these were stored as a data set. The machine learning algorithm applied to this data set was followed by utilising a data mining process to explore the characteristics of the Twitter feed users. The network of the users was mapped and the individual level of interactivity and network density were calculated. Findings The machine learning algorithm revealed 12 themes all of which were underpinned by the coalition of Arab countries blockade of Qatar. The data mining analysis revealed that the tweets could be clustered in three clusters, the main cluster included users with a large number of followers and friends but who did not mention other users in their tweets. The social network analysis revealed that whilst a large proportion of users engaged in direct messages with others, the network ties between them were not registered as strong. Borum (2011) notes that invoking grievances is the first step in the radicalisation process. It is hoped that by understanding these grievances, the study will shed light on what radical groups could invoke to win the sympathy of aggrieved people. In combination, the machine learning algorithm offered insights into the grievances expressed within the tweets in an Arabian context. The data mining and the social network analyses revealed the characteristics of the Twitter users highlighting identifying and managing early intervention of radicalisation.