This chapter considers three types of online data available for researchers. First, it looks at machine learning and its use when considering the vast amount of data available to detect indicators of involvement in terrorism. Next, the chapter considers case studies and their use when addressing ‘how’ and ‘why’ questions. Given the difficulty of research with this population, case studies lend themselves to analysis of an individual terrorist’s behaviour. Finally, netnography (an ethnographic study of online communities) is reviewed with the argument that it has furthered our understanding of radicalisation. This area of research considers the intersection of online and offline relationships in mobilising people towards radicalisation. The chapter concludes with a review of the benefits and weaknesses of these different online research methods.
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Front Matter
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Part I: Major perspectives in terrorism studies research
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Part II: Assessing theory, some methods and approaches
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Part III: Controversies and debates
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Part IV: The future of terrorism studies
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Back Matter
