USING SNA VISUALIZATIONS TO DEPICT SUSPICIOUS SOCIAL MEDIA USERS: A FORAGE FOR LAW ENFORCERS
Authors: Dr. Lamek Kiprutto Ronoh
Affiliation: Rongo University,School of information, communication and media studies Department of Information Science and Informatics
Keywords: Social Media, visualizations Social Network Analysis, actor(s), node(s)
ABSTRACT. The evident increase in the sophistication of cyber criminals has a significant impact that can threaten the national security if it goes unabated. Presently, use of social media in mining crucial digital or forensic evidence by law enforcement bodies in Kenya is a novel idea that needs to be explored and implemented. The study’s objective was to demonstrate how Social Network Analysis (SNA) can be used as an investigative tool to mine, analyse data from selected online social media users and present digital forensic evidence to aid law enforcement in Kenya. Particularly, the study aimed at identifying high degree nodes in the network and profiling them using visualizations. NodeXL software was used to mine and analyse data. Computation of centrality measures, network clusters, cliques were presented using both infographic visualizations and centrality metrics of the respondents on egocentric networks focal communication paths through which information flows in the network were also depicted. The discoveries of this study indicated that Social Network Analysis is an essential and supplementary tool that can be employed by law enforcement agencies and related stakeholders to mine, analyse and present court accepted digital forensic evidence. The findings presented in this research illustrates how social network analysis can be used to determine the interpersonal connections, importance of actors in a given social network and detect communities of people and principally how law enforcement agencies can utilize this technique in identifying and tracking suspicious characters and ultimately help in maintaining law and order. SNA ought to be embraced as a supplement of conventional investigation, not necessarily replacing it.
1. Acquisti, A., Gross, R., & Stutzman, F. (2011). Faces of facebook: Privacy in the age of augmented reality.
2. Arnaboldi, V., Conti, M., Passarella, A., & Pezzoni, F. (2013, April). Ego networks intwitter: an experimental analysis. In Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference on (pp. 229-234). IEEE.
3. Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology, 2(1), 113-120.
4. Faust, K. (2006). Comparing social networks: size, density, and local structure. Metodoloski zvezki, 3(2), 185.
5. Faust, K., & Fitzhugh, S. (2012). Social Network Analysis: An Introduction. Recuperado de: https://www. icpsr. umich. edu/icpsrweb/sumprog/syllabi/97573 [Consulta: 2014, 12 de agosto].
6. Ferrara, E., De Meo, P., Catanese, S., & Fiumara, G. (2014). Visualizing criminal networks reconstructed from mobile phone records. arXiv preprint arXiv:1407.2837.
7. Global Justice Information Sharing Initiative.(2013).Developing a Policy on the Use of Social Media in Intelligence and Investigative Activities. Guidance and Recommendations.
8. Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 73 (6), 1360-1380.
9. Gupta, R.,. & Brooks, H. (2015).Using Social media for global Security. John Wiley & Sons Inc., Indianapolis.
10. Hanneman, R. A., & Riddle, M. (2005). Ego networks. Introduction to Social Network Methods. CA: Riverside. Analytictech. com.
11. Hansen, D., Shneiderman, B. & Smith, A,M. (2011). Analyzing Social Media Networks With Nodexl: Insights From A Connected World. Elsevier Inc., Massachusetts.
12. Hoppe, B., & Reinelt, C. (2010). Social network analysis and the evaluation of leadership networks. The Leadership Quarterly, 21(4), 600-619.
13. Kirchner, C., & Gade, J. (2011). Implementing social network analysis for fraud prevention. CGI Group Ind.
14. Long, J. C., Cunningham, F. C., & Braithwaite, J. (2013). Bridges, brokers and boundary spanners in collaborative networks: a systematic review. BMC health services research, 13(1), 158.
15. Passmore, D. L. (2011). Social network analysis: Theory and applications. Institute for Research in Training & Development–IRTD.
16. Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43-52.
17. Martino, F., & Spoto, A. (2006). Social network analysis: A brief theoretical review and further perspectives in the study of information technology. PsychNology Journal, 4(1), 53-86.
18. Mena, J. (2003). Investigative data mining for security and criminal detection. Butterworth- Heinemann.
19. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1), 415-444.
20. Mulazzani, M., Huber, M., & Weippl, E. (2012, January). Data visualization for social network forensics. In IFIP International Conference on Digital Forensics (pp. 115-126). Springer Berlin Heidelberg.
21. Murphy, J. P., & Fontecilla, A. (2013). Social media evidence in government investigations and criminal proceedings: A frontier of new legal issues. Rich. JL & Tech., 19, 11-14.
22. Nagl, J. A., Amos, J. F., Sewall, S., & Petraeus, D. H. (2008). The US Army/Marine Corps Counterinsurgency Field Manual. University of Chicago Press.