Uma Abordagem Baseada em Redes Complexas para Análise de Depoimentos Legais
Resumo
Neste trabalho, apresentamos uma abordagem para identificar entidades e relacionamentos influentes em depoimentos legais. Como estudo de caso, modelamos cinco delações públicas da operação Lava Jato como redes (complexas), a fim de representar as relações sociais entre as pessoas citadas nos depoimentos. Para identificar entidades importantes, utilizamos medidas de centralidade (e.g., eigenvector, betweenness) selecionadas com base na entropia de Shannon. Os relacionamentos influentes foram detectados pelo algoritmo de Louvain. Nossos resultados mostram que os nós (pessoas) identificados como influentes nas redes se tornaram alvo de investigações. Os autores declaram que não são objetivos deste trabalho servir de evidência legal ou determinar o grau de veracidade dos testemunhos e justiça das investigações. O trabalho simplesmente apresenta uma análise social baseada nas estruturas topológicas das redes que representam depoimentos, quer esses depoimentos representem a verdade ou não.
Referências
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.