Using Deep Learning Neural Networks to Predict Violent vs. Nonviolent Extremist Behaviors
Recent analyses of radicalization processes have shown that extremist attitudes and violent behavior may be related in some cases, but are rarely collinear. It therefore benefits analysts of political violence to leverage tools that assist in the distinction of characteristics that might move an ind...
| Autor principal: | |
|---|---|
| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2025
|
| En: |
Terrorism and political violence
Año: 2025, Volumen: 37, Número: 6, Páginas: 834-856 |
| Acceso en línea: |
Volltext (lizenzpflichtig) |
| Journals Online & Print: | Gargar...
|
| Verificar disponibilidad: | HBZ Gateway |
| Palabras clave: |
| Sumario: | Recent analyses of radicalization processes have shown that extremist attitudes and violent behavior may be related in some cases, but are rarely collinear. It therefore benefits analysts of political violence to leverage tools that assist in the distinction of characteristics that might move an individual towards violence (vs. nonviolence) in support of their beliefs. To this end, the current study explores the efficacy of deep learning neural networks for classifying extremists as potentially violent or nonviolent based on dozens of common predictors derived from various perspectives on radicalization. Specifically, this study uses 337 predictors from the Profiles of Individual Radicalization in the U.S. dataset to populate a neural network with two hidden layers composed of four processing nodes. The model correctly predicted whether an individual engaged in violence (or not) in 94.2 percent of cases, on average. Analyses further identified several predictors that were most important in classifying violent and nonviolent cases. These analyses demonstrate neural networks may be effective tools in the study of radicalization and extremism, particularly regarding the disaggregation of salient outcomes. |
|---|---|
| ISSN: | 1556-1836 |
| DOI: | 10.1080/09546553.2024.2376639 |
