Authors:
Yingjie Liu
1
;
Gregory Wert
1
;
Benjamin Greenawald
1
;
Mohammad Al Boni
2
and
Donald E. Brown
3
Affiliations:
1
Data Science Institute, University of Virginia and U.S.A.
;
2
Department of Systems and Information Engineering, University of Virginia and U.S.A.
;
3
Data Science Institute, University of Virginia, U.S.A., Department of Systems and Information Engineering, University of Virginia and U.S.A.
Keyword(s):
Text Analysis, Natural Language Processing, Convolutional Neural Networks, Bidirectional Recurrent Neural Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
Abstract:
Groups advocating violence have caused significant destruction to individuals and societies. To combat this, governmental and non-governmental organizations must quickly identify violent groups and limit their exposure. While some groups are well-known for their violence, smaller, less recognized groups are difficult to classify. However, using texts from these groups, we may be able to identify them. This paper applies text analysis techniques to differentiate violent and non-violent groups using discourses from various value-motivated groups. Significantly, the algorithms are constructed to be language-agnostic. The results show that deep learning models outperform traditional models. Our models achieve high accuracy when fairly trained only on data from other groups. Additionally, the results indicate that the models achieve better performance by removing groups with a large amount of documents that can bias the classification. This study shows promise in using scalable, language-
independent techniques to effectively identify violent value-motivated groups.
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