An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity

The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 17877 - 17891
Main Authors Araque, Oscar, Iglesias, Carlos A.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Affective computing
Computational modeling
Digital media
Emotions
Feature extraction
Machine learning
natural language processing
Radicalism
Semantics
Similarity
Social networking (online)
Social networks
Task analysis
Terrorism
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Summary:The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2967219