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

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #73926 Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
sameAs	Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
http://eprints.org/ontology/hasAccepted	Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (PDF)
http://eprints.org/ontology/hasDocument	Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (PDF) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (Other) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (Other) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (Other) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (Other) Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (Other)
dc:hasVersion	Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter (PDF)
Title	Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-07-03
Creator	Michael Goldsmith Mariam Nouh Jason R. C. Nurse
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	2019 IEEE International Conference on Intelligence and Security Informatics (ISI) https://kar.kent.ac.uk/id/repository
Subject	H Social Sciences QA Mathematics (inc Computing science) T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/73926#authors
presented at	17th IEEE International Conference on Intelligence and Security Informatics (ISI)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #73926 Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

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