Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases.

ABSTRACT: Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was foun

SUBMITTER: Masias VH 

PROVIDER: S-EPMC4732755 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature