title: 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 found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.
authors.affiliation: Department of Management Control and Information Systems, Universidad de Chile, Santiago, Chile.
authors.forename: Víctor Hugo
authors.initials: VH
authors.lastname: Masías
authors.affiliation: Faculty of Economics and Business, Universidad Finis Terrae, Santiago, Chile.
authors.forename: Mauricio
authors.initials: M
authors.lastname: Valle
authors.affiliation: Centre international de criminologie comparée (CICC), École de criminologie, Université de Montréal, Montréal (Québec), Canada.
authors.forename: Carlo
authors.initials: C
authors.lastname: Morselli
authors.affiliation: CEDYTEC, Dirección de Investigación, Universidad Bernardo OHiggins, Santiago, Chile.
authors.forename: Fernando
authors.initials: F
authors.lastname: Crespo
authors.affiliation: Departamento de Diseño y Manufactura (DIMA), Universidad Técnica Federico Santa María, Viña del Mar, Chile.
authors.forename: Augusto
authors.initials: A
authors.lastname: Vargas
authors.affiliation: Department of Management Control and Information Systems, Universidad de Chile, Santiago, Chile.
authors.forename: Sigifredo
authors.initials: S
authors.lastname: Laengle
mesh_terms: D000465:Algorithms; D003415:Crime / Q000523:psychology*; D003416:Criminal Law; D006801:Humans; D008962:Models, Theoretical; D060756:Social Networking
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