A Complex Networks-Based Approach to Legal Testimony Analysis
Abstract
In this paper, we present a methodology to identify influential entities and important relationships in legal testimonies. First, we model the testimonies as complex networks that represent the social relationships among the people that are referred to in the testimonies. Then, we use: (1) centrality metrics (e.g., eigenvector, betweenness) to identify important entities; (2) community detection algorithms to find correlated groups (social nuclei). In addition, we propose the use of the Shannon entropy to quantify the discrimination level of the centrality metrics. As a case study, we modeled five public plea agreements of the Lava Jato Operation. The results show that the entities that we identify as influential have become targets of investigations. We declare that this work is not intended to be used as legal evidence nor to determine the level of fairness of the testimonies and investigations. We simply present a social analysis that is based on the topological structures of the networks that represent testimonies, whether or not those testimonies represent the truth.
References
Blondel, V., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008.
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D. (2006). Complex networks: Structure and dynamics. Physics reports, 424(4):175–308.
Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social networks, 29(4):555–564.
Brandão, M. and Moro, M. (2016). Social professional networks: A survey and taxonomy. Computer Communications, 100.
Bright, D., Hughes, C., and Chalmers, J. (2012). Illuminating dark networks: A social network analysis of an australian drug trafficking syndicate. Crime, Law and Social Change, 57(2):151–176.
Calderoni, F. and Piccardi, C. (2014). Uncovering the structure of criminal organizations by community analysis: The infinito network. In Proc. of 10th Int’l Conf. on Signal-Image Technology and Internet-Based Systems, pages 301–308.
Campana, P. (2016). Explaining criminal networks: Strategies and potential pitfalls. Methodological Innovations, 9:1–10.
Freeman, L. (1978). Centrality in social networks conceptual clarification. Social networks, 1(3):215–239.
Kossinets, G. and Watts, D. (2009). Origins of homophily in an evolving social network. American journal of sociology, 115(2):405–450.
Lesne, A. (2014). Shannon entropy: A rigorous notion at the crossroads between probability, information theory, dynamical systems and statistical physics. Mathematical Structures in Computer Science, 24(03):240–311.
Liu, X., Patacchini, E., Zenou, Y., and Lee, L.-F. (2012). Criminal networks: Who is the key player?
Mancuso, M. (2014). Not all madams have a central role: analysis of a nigerian sex trafficking network. Trends in Organized Crime, 17(1-2):66–88.
Masys, A. (2013). Networks and network analysis for defence and security. In Proc. of the Int’l Conf. on Advances in Social Networks Analysis and Mining, pages 1479–1480.
Morselli, C. (2010). Assessing vulnerable and strategic positions in a criminal network. Journal of Contemporary Criminal Justice, 26(4):382–392.
Morselli, C. (2013). Crime and Networks. Routledge.
Morselli, C. and Savoie-Gargiso, I. (2014). Coercion, control, and cooperation in a prostitution ring. The ANNALS of the American Academy of Political and Social Science, 653(1):247–265.
Otte, E. and Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of information Science, 28(6):441–453.
Rostami, A. and Mondani, H. (2015). The complexity of crime network data: A case study of its consequences for crime control and the study of networks. PloS One, 10(3):e0119309.
Snijders, T. and Baerveldt, C. (2003). A multilevel network study of the effects of delinquent behavior on friendship evolution. Journal of mathematical sociology, 27(2-3):123–151.
