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Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

Nouh, Mariam, Nurse, Jason R. C., Goldsmith, Michael (2019) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter. In: 2019 IEEE International Conference on Intelligence and Security Informatics (ISI). 17th IEEE International Conference on Intelligence and Security Informatics (ISI). . IEEE (doi:10.1109/ISI.2019.8823548) (KAR id:73926)

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http://dx.doi.org/10.1109/ISI.2019.8823548

Abstract

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/ISI.2019.8823548
Subjects: H Social Sciences
Q Science > QA Mathematics (inc Computing science)
T Technology
Divisions: Faculties > Sciences > School of Computing > Security Group
Faculties > Social Sciences > School of Psychology
Faculties > Social Sciences > School of Social Policy Sociology and Social Research
Depositing User: Jason Nurse
Date Deposited: 15 May 2019 17:06 UTC
Last Modified: 25 Sep 2019 14:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/73926 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
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