Radical text detection based on stylometry

Pablo Marsal, Álvaro de and Araque Iborra, Óscar and Iglesias Fernández, Carlos Ángel (2020). Radical text detection based on stylometry. In: "6th International Conference on Information Systems Security and Privacy", 25/02/2020 - 27/02/2020, Valetta, Italia. ISBN 978-989-758-399-5. pp. 1-8. https://doi.org/10.5220/0008971205240531.

Description

Title: Radical text detection based on stylometry
Author/s:
  • Pablo Marsal, Álvaro de
  • Araque Iborra, Óscar
  • Iglesias Fernández, Carlos Ángel
Item Type: Presentation at Congress or Conference (Article)
Event Title: 6th International Conference on Information Systems Security and Privacy
Event Dates: 25/02/2020 - 27/02/2020
Event Location: Valetta, Italia
Title of Book: Proceedings of 6th International Conference on Information Systems Security and Privacy
Date: February 2020
ISBN: 978-989-758-399-5
Subjects:
Freetext Keywords: Radicalism; Terrorism; ISIS; Dabiq; Rumiyah; Stylometry; Machine Learning
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The Internet has become an effective tool for terrorist and radical groups to spread their propaganda. One of the current problems is to detect these radical messages in order to block them or promote counter-narratives.In this work, we propose the use of stylometric methods for characterizing radical messages. We have used a machine learning approach to classify radical texts based on a corpus of news from radical sources such as the so-called ISIS online magazines Dabiq and Rumiyah, as well as news from general newspapers. The results show that stylometric features are effective for radical text classification.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020740934TRIVALENTUNIVERSITA DEGLI STUDI ROMA TRETerrorism pReventIon Via rAdicaLisation countEr-NarraTive

More information

Item ID: 64621
DC Identifier: https://oa.upm.es/64621/
OAI Identifier: oai:oa.upm.es:64621
DOI: 10.5220/0008971205240531
Official URL: https://www.insticc.org/node/TechnicalProgram/icissp/2020/presentationDetails/89712
Deposited by: Memoria Investigacion
Deposited on: 27 Mar 2021 08:43
Last Modified: 27 Mar 2021 08:43
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