Abstract
To address the rising tide of religious violence as it affects U.S. Military deployments, the Army requires analytic methods that can be generalized to predict religious group violence around the globe and scalable to the number of potential groups in an area of operation. Current computational methods based on semantics and topics are lacking in predictive performance for the generalized problem and topic modeling performs poorly in predicting the tolerance level of new groups towards U.S. Military presence. The research in this paper aims to discover the association between religious speech and behaviors and provide a foundation for proactive engagement with these groups. The approach builds on the work from ethnolinquistics to model how things are said (performative analysis) rather than word meanings (semantic analysis). Recent research has developed computational approaches to streamline the manually intensive performative analysis of religious text. While producing promising results, these computational methods lack systematic optimization of the hyperparamters in the learning algorithms. Hence, we do not know the sensitivity of the results to parameter settings. This paper reports on results for predicting religious tolerance by optimizing the parameters in the signal processing algorithms and shows that the predictive power of performative approach is robust to parameter settings.
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Brown, D.E., McIntyre, H., Grazaitis, P.J., Hazell, R.M., Venuti, N. (2017). Hyperparameter Optimization for Predicting the Tolerance Level of Religious Discourse. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_41
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