A comparison of language-dependent and language-independent models for violence prediction

The nature of violence changes with developments in politics, religion, and technology. This poses challenges to governmental and non-governmental organizations responsible for bringing forth timely strategies for engaging with groups advocating violence. While some groups are well-known for their violence, other groups' characteristics vary throughout time and across regions, which hampers traditional decision-making processes, taking time and resources that organizations do not always have. As such, a scalable and effective method for identifying violent groups becomes imperative. This paper applies text analysis techniques to differentiate violent and non-violent groups using English text from various value-based groups. The models presented in this paper achieved accuracies of at least 71% and as high as 83%. The results demonstrate that text analysis provides a powerful predictive solution for winnowing the violent groups from the non-violent ones. In addition, by incorporating natural language processing tools, language-dependent models show a slight 2% improvement in accuracy over language-independent models. The overall similar performance of language-dependent and language-independent models suggests the two approaches are comparable alternatives to each other.