Predicting Violent Behavior using Language Agnostic Models

Yingjie Liu, Gregory Wert, Benjamin Greenawald, Mohammad Al Boni, Donald E. Brown

2018

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.

Paper Citation

in Harvard Style

Liu Y., Wert G., Greenawald B., Boni M. and Brown D. (2018). Predicting Violent Behavior using Language Agnostic Models. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR; ISBN 978-989-758-330-8, SciTePress, pages 102-109. DOI: 10.5220/0006933701020109

in Bibtex Style

@conference{kdir18,
author={Yingjie Liu and Gregory Wert and Benjamin Greenawald and Mohammad Al Boni and Donald E. Brown},
title={Predicting Violent Behavior using Language Agnostic Models},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR},
year={2018},
pages={102-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006933701020109},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR
TI - Predicting Violent Behavior using Language Agnostic Models
SN - 978-989-758-330-8
AU - Liu Y.
AU - Wert G.
AU - Greenawald B.
AU - Boni M.
AU - Brown D.
PY - 2018
SP - 102
EP - 109
DO - 10.5220/0006933701020109
PB - SciTePress