The Application of Statistical Relational Learning to a Database of Criminal and Terrorist Activity

Abstract

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of terrorist groups or as input to inform human decision makers.

Analysis of Competing Hypothesis for Investigating Lone Wolf Terrorist

“…In [3] statistical relational learning is applied to a database of criminal and terrorist activity to predict attributes and event outcomes. These methods could be useful to predict outcomes of radicalization processes of possible lone wolves as well.…”

Social networks and statistical relational learning: a survey

“…The initial seed for this iterative procedure is selected by identifying communities in the network, and then selecting a representative from each community. Delaney et al (2010) applied SRL algorithms to predict leadership roles of individuals in a group based on patterns of activity, communication, and individual attributes. The authors focused on data collection on criminal and terror networks whose straightforward use includes manual analysis of groups and individuals involved in nefarious activity to inform key decision makers tasked with preventing future bombings or other violent attacks.…”