A framework for the forensic analysis of user interaction with social media

International Journal of Digital Crime and Forensics

Volumn 4, Issue 4, 2012, Pages 15-30

  Haggerty, John ^a   Casson, Mark C ^b   Haggerty, Sheryllynne ^c   Taylor, Mark J ^d  

^a UNIVERSITY OF SALFORD   (United Kingdom)

^b UNIVERSITY OF READING   (United Kingdom)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^d LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

Author keywords

Digital forensics; Regression analysis; Social media; Social networks; User interaction

Indexed keywords

DIGITAL EVIDENCE; DIGITAL FORENSIC; FORENSIC ANALYSIS; IDENTIFICATION OF INDIVIDUALS; SOCIAL MEDIA; TEMPORAL DIMENSIONS; USER ENGAGEMENT; USER INTERACTION;

COMPUTER FORENSICS; REGRESSION ANALYSIS;

SOCIAL NETWORKING (ONLINE);

EID: 84880568694     PISSN: 19416210     EISSN: 19416229     Source Type: Journal    
DOI: 10.4018/jdcf.2012100102     Document Type: Article

References (28)

1. Access Data. (2012). FTK: Forensic tool kit. Retrieved from http://www.accessdata.com
2. Beck, K. (2011). Analyzing tweets to identify malicious messages. In Proceedings of the International Conference on Electro/Information Technology (pp. 1-5).
3. Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., & Pedreschi, D. (2010). Towards discovery of eras in social networks. In Proceedings of the International Workshop on Modeling, Managing and Mining of Evolving Social Networks, in conjunction with the International Conference on Data Engineering (pp. 278-281).
4. Boston, D. J., & Borcea, C. (2011). TIE: Temporal interaction explorer for co-presence communities. In Proceedings of the 9th International Conference on Dependable, Autonomic and Secure Computing (pp. 854-863).
5. Correa, C., Crnovrsanin, T., & Ma, K.-L. (2012). Visual reasoning about social networks using centrality sensitivity. IEEE Transactions on Visualization and Computer Graphics, 18(1), 106-120. doi:10.1109/TVCG.2010.260
6. Dugan, L. (2012, February 21). Twitter to surpass 500 million registered users on Wednesday. Retrieved from http://www.mediabistro.com/alltwitter/500- million-registered-users-b18842
7. Facebook. (2012). Newsroom key facts. Retrieved from http://newsroom.fb. com/content/default. aspx?NewsAreaId=22
8. Falkowski, T., Bartelheimer, J., & Spiliopoulou, M. (2006). Mining and visualizing the evolution of subgroups in social networks. In Proceedings of the International Conference on Web Intelligence (pp. 52-58).
9. Gou, L., & Zhang, X. (2011). Tree Net Viz: Revealing patterns of networks over tree structures. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2449-2458. doi:10.1109/TVCG.2011.247
10. Guidance Software. (2012). Encase. Retrieved from http://www. guidancesoftware.com
11. Hadjidj, R., Debbabi, M., Lounis, H., Iqbal, F., Szporer, A., & Benredjem, D. (2009). Towards an integrated e-mail forensic analysis framework. Digital Investigation, 5(3-4), 124-137. doi:10.1016/j. diin.2009.01.004
12. Haggerty, J., & Haggerty, S. (2011). Temporal social network analysis for historians: A case study. In Proceedings of the International Conference on Visualization Theory and Applications (pp. 207-217).
13. Haggerty, J., Karran, A. J., Lamb, D. J., & Taylor, M. J. (2011). A framework for the forensic investigation of unstructured email relationship data. International Journal of Digital Crime and Forensics, 3(3), 1-18. doi:10.4018/jdcf.2011070101
14. Henseler, H. (2010). Network-based filtering for large email collections in e-discovery. Artificial Intelligence and Law, 18(4), 413-430. doi:10.1007/s10506-010-9099-3
15. Horng, S.-M. (2010). Analysis of users behavior on Web 2.0 social network sites: An empirical study. In Proceedings of the 7th International Conference on Information Technology: New Generations (pp. 454-459).
16. Houghton, D. J., & Joinson, A. N. (2012). Linguistic markers of secrets and sensitive self-disclosure in Twitter. In Proceedings of the 45th Hawaii International Conference on Systems Science (pp. 3480-3489).
17. Hu, B., & Gong, J. (2010). Modeling individual-based social network with spatial-temporal information. In Proceedings of the International Conference on Management and Service Science (pp. 1-4).
18. Kim, U. (2007). Analysis of personal email networks using spectral decomposition. International Journal of Computer Science and Network Security, 7(4), 185-188.
19. Nirkhi, S. M., Dharaskar, R. V., & Thakre, V. M. (2012). Analysis of online messages for identity tracing in cybercrime investigation. In Proceedings of the International Conference on Cyber Security, Cyber Warfare and Digital Forensics (pp. 300-305).
20. Perer, A., & Schneiderman, B. (2009). Integrating statistics and visualization for exploratory power: From long-term case studies to design guidelines. IEEE Computer Graphics and Applications, 29(3), 39-51. doi:10.1109/MCG.2009.44
21. Southampton Facebook riot messages man jailed. (2012, June 21). BBC News. Retrieved from http://www.bbc.co.uk/news/uk-england-hampshire-18536940
22. Taylor, M., Haggerty, J., & Gresty, D. (2009). The legal aspects of corporate email investigations. Computer Law & Security Report, 25(4), 372-376. doi:10.1016/j.clsr.2009.05.006
23. Twitter Engineering Blog. (2011, May 31). The engineering behind Twitter's new search experience. Retrieved from http://engineering.twitter.com/ 2011/05/engineering-behind-twitters-new-search.html
24. Vlado, A. (2012). Networks/Pajek Program for large network analysis. Retrieved from http://vlado. fmf.uni-lj.si/pub/networks/pajek/
25. Wang, W., & Daniels, T. E. (2008). A graph based approach toward network forensic analysis. ACM Transactions on Information and System Security, 12(1), 401-433. doi:10.1145/1410234.1410238
26. Wei, C., Sprague, A., Warner, G., & Skjellum, A. (2008). Mining spam e-mail to identify common origins for forensic application. In Proceedings of the ACM Symposium on Applied Computing (pp. 1433-1437).
27. Wiil, U. K., Gniadek, J., & Memon, N. (2010). Measuring link importance in terrorist networks. In Proceedings of the International Conference on Social Networks Analysis and Mining (pp. 225-232).
28. Zhang, Z., & Li, X. (2010). Controversy is marketing: Mining sentiments in social media. In Proceedings of the 43rd Hawaii International Conference on Systems Science (pp. 1-10).