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Understanding Online Radicalisation Using Data Science

Understanding Online Radicalisation Using Data Science

Yeslam Al-Saggaf
Copyright: © 2019 |Pages: 16
ISBN13: 9781522571193|ISBN10: 1522571191|EISBN13: 9781522571209
DOI: 10.4018/978-1-5225-7119-3.ch025
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MLA

Al-Saggaf, Yeslam. "Understanding Online Radicalisation Using Data Science." Violent Extremism: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2019, pp. 471-486. https://doi.org/10.4018/978-1-5225-7119-3.ch025

APA

Al-Saggaf, Y. (2019). Understanding Online Radicalisation Using Data Science. In I. Management Association (Ed.), Violent Extremism: Breakthroughs in Research and Practice (pp. 471-486). IGI Global. https://doi.org/10.4018/978-1-5225-7119-3.ch025

Chicago

Al-Saggaf, Yeslam. "Understanding Online Radicalisation Using Data Science." In Violent Extremism: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 471-486. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7119-3.ch025

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Abstract

What characterises social media radicals? And why some people become attracted to radicalisation? To explore answers to these questions, a number of tweets posted by a group of suspected radicals tweeting in Arabic were analysed using social network analysis and machine learning. The study revealed that these suspected radicals' networks showed significant interaction with others; but this interactivity is only significant quantitatively as the interaction is not reciprocated. With regards to why these suspected radicals became attracted to radicalisation, Topic Modelling revealed these suspected radicals' tweets underpinned a perceived injustice that they believed the Secret Police and the Government inflicted upon them. Overall, the study has shown that data science tools have the potential to inform our understanding of online radicalisation. It is hoped this exploratory study will be the basis for a future study in which the research questions will be answered using a larger sample.

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