Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects

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Keywords: SNA, Twitterforensics, Secondary Key Actor, Key Actor

Abstract

SNA (Social Network Analysis) is a modeling method for users which is symbolized by points (nodes) and interactions between users are represented by lines (edges). This method is needed to see patterns of social interaction in the network starting with finding out who the key actors are. The novelty of this study lies in the expansion of the analysis of other suspects, not only key actors identified during this time. This method performs a narrowed network mapping by examining only nodes connected to key actors. Secondary key actors no longer use centrality but use weight indicators at the edges. A case study using the hashtag "Manchester United" on the social media platform Twitter was conducted in the study. The results of the Social Network Analysis (SNA) revealed that @david_ornstein accounts are key actors with centrality of 2298 degrees. Another approach found @hadrien_grenier, @footballforall, @theutdjournal accounts had a particularly high intensity of interaction with key actors. The intensity of communication between secondary actors and key actors is close to or above the weighted value of 50. The results of this analysis can be used to suspect other potential suspects who have strong ties to key actors by looking.

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Author Biographies

Irwan Sembiring, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

Ade Iriani, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

Suharyadi, Universitas Kristen Satya Wacana

Faculty of Information Technology,
Universitas Kristen Satya Wacana

References

M. O. Ibrohim and I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” 2019. doi: 10.18653/v1/w19-3506.

“Statistik laporan masyarakat,” 2021. https://patrolisiber.id/ (accessed Jun. 20, 2022).

W. Anwar, I. S. Bajwa, M. A. Choudhary, and S. Ramzan, “An empirical study on forensic analysis of Urdu text using LDA-based authorship attribution,” IEEE Access, vol. 7, pp. 3224–3234, 2019, doi: 10.1109/ACCESS.2018.2885011.

X. Du, N. A. Le-Khac, and M. Scanlon, “Evaluation of digital forensic process models with respect to digital forensics as a service,” in European Conference on Information Warfare and Security, ECCWS, 2017, pp. 573–581.

A. Agarwal, M. Gupta, S. Gupta, and S. C. Gupta, “Systematic digital forensic investigation model,” Int. J. Comput. Sci. Secur., vol. 5, no. 1, pp. 118–131, 2011, [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.227.8647&rep=rep1&type=pdf

M. D. Kohn, M. M. Eloff, and J. H. P. Eloff, “Integrated digital forensic process model,” Comput. Secur., vol. 38, 2013, doi: 10.1016/j.cose.2013.05.001.

A. Dimitriadis, N. Ivezic, B. Kulvatunyou, and I. Mavridis, “D4I - Digital forensics framework for reviewing and investigating cyber attacks,” Array, vol. 5, p. 100015, Mar. 2020, doi: 10.1016/J.ARRAY.2019.100015.

A. Aslam, S. M. Maher, L. Kanwal, and M. A. Shah, “An aspect of internet of things security: Analysis of digital fingerprinting of generic Twittersessions by using forensic tool,” ICAC 2019 - 2019 25th IEEE Int. Conf. Autom. Comput., no. September, pp. 1–5, 2019, doi: 10.23919/IConAC.2019.8895172.

Y. Wang, H. Sun, Y. Zhao, W. Zhou, and S. Zhu, “A Heterogeneous Graph Embedding Framework for Location-Based Social Network Analysis in Smart Cities,” IEEE Trans. Ind. Informatics, vol. 16, no. 4, pp. 2747–2755, 2020, doi: 10.1109/TII.2019.2953973.

I. Sembiring, Suharyadi, A. Iriani, J. V. B. Ginting, and J. A. Ginting, “A Novel Approach to Network Forensic Analysis: Combining Packet Capture Data and Social Network Analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 3, pp. 466–472, 2023, doi: 10.14569/IJACSA.2023.0140353.

G. Bissias, B. N. Levine, M. Liberatore, and S. Prusty, “Forensic Identification of Anonymous Sources in OneSwarm,” IEEE Trans. Dependable Secur. Comput., vol. 14, no. 6, pp. 620–632, 2017, doi: 10.1109/TDSC.2015.2497706.

P. Lewulis, “Digital forensic standards and digital evidence in Polish criminal proceedings. An updated definition of digital evidence in forensic science,” Int. J. Electron. Secur. Digit. Forensics, vol. 13, no. 4, 2021, doi: 10.1504/IJESDF.2021.116024.

D. Cozzolino and L. Verdoliva, “Noiseprint: A CNN-Based Camera Model Fingerprint,” IEEE Trans. Inf. Forensics Secur., vol. 15, no. 1, pp. 144–159, 2020, doi: 10.1109/TIFS.2019.2916364.

P. Reedy, “Interpol review of digital evidence 2016 - 2019,” Forensic Sci. Int. Synerg., vol. 2, pp. 489–520, 2020, doi: 10.1016/j.fsisyn.2020.01.015.

V. R. Kebande, P. P. Mudau, R. A. Ikuesan, H. S. Venter, and K.-K. R. Choo, “Holistic digital forensic readiness framework for IoT-enabled organizations,” Forensic Sci. Int. Reports, vol. 2, p. 100117, Dec. 2020, doi: 10.1016/j.fsir.2020.100117.

G. Horsman and N. Sunde, “Unboxing the digital forensic investigation process,” Sci. Justice, vol. 62, no. 2, pp. 171–180, Mar. 2022, doi: 10.1016/J.SCIJUS.2022.01.002.

D. Mothi, H. Janicke, and I. Wagner, “A novel principle to validate digital forensic models,” Forensic Sci. Int. Digit. Investig., vol. 33, p. 200904, Jun. 2020, doi: 10.1016/J.FSIDI.2020.200904.

Q. Li, G. Sovernigo, and X. Lin, “BlackFeather: A framework for background noise forensics,” Forensic Sci. Int. Digit. Investig., vol. 42, p. 301396, Jul. 2022, doi: 10.1016/j.fsidi.2022.301396.

N. M. Karie, V. R. Kebande, and H. S. Venter, “Diverging deep learning cognitive computing techniques into cyber forensics,” Forensic Sci. Int. Synerg., vol. 1, pp. 61–67, Jan. 2019, doi: 10.1016/J.FSISYN.2019.03.006.

A. Mohammed Ali and A. Kadhim Farhan, “A novel improvement with an effective expansion to enhance the MD5 hash function for verification of a secure E-Document,” IEEE Access, vol. 8, pp. 80290–80304, 2020, doi: 10.1109/ACCESS.2020.2989050.

S. Long, “A Comparative Analysis of the Application of Hashing Encryption Algorithms for MD5, SHA-1, and SHA-512,” in Journal of Physics: Conference Series, 2019, vol. 1314, no. 1. doi: 10.1088/1742-6596/1314/1/012210.

DAC Janet Williams QPM, “Revised Good Practice Guide for Digital Evidence_Vers 5_Oct 2011_Website,” 2012, [Online]. Available: https://www.npcc.police.uk/documents/crime/2014/Revised Good Practice Guide for Digital Evidence_Vers 5_Oct 2011_Website.pdf

S. Sen Zhang, X. Liang, Y. D. Wei, and X. Zhang, “On Structural Features, User Social Behavior, and Kinship Discrimination in Communication Social Networks,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 425–436, 2020, doi: 10.1109/TCSS.2019.2962231.

A. Matakos, C. Aslay, E. Galbrun, and A. Gionis, “Maximizing the Diversity of Exposure in a Social Network,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 9, pp. 4357–4370, 2022, doi: 10.1109/TKDE.2020.3038711.

M. Mirtaheri, S. Abu-El-Haija, F. Morstatter, G. Ver Steeg, and A. Galstyan, “Identifying and Analyzing Cryptocurrency Manipulations in Social Media,” IEEE Trans. Comput. Soc. Syst., vol. 8, no. 3, pp. 607–617, 2021, doi: 10.1109/TCSS.2021.3059286.

D. Vimalajeewa, S. Balasubramaniam, B. O’Brien, C. Kulatunga, and D. P. Berry, “Leveraging Social Network Analysis for Characterizing Cohesion of Human-Managed Animals,” IEEE Trans. Comput. Soc. Syst., vol. 6, no. 2, pp. 323–337, 2019, doi: 10.1109/TCSS.2019.2902456.

A. A. Al-Shargabi and A. Selmi, “Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic,” IEEE Access, vol. 9, pp. 90616–90630, 2021, doi: 10.1109/access.2021.3091537.

M. Bérubé, T. U. Tang, F. Fortin, S. Ozalp, M. L. Williams, and P. Burnap, “Social media forensics applied to assessment of post–critical incident social reaction: The case of the 2017 Manchester Arena terrorist attack,” Forensic Sci. Int., vol. 313, 2020, doi: 10.1016/j.forsciint.2020.110364.

A. Umrani, Y. Javed, and M. Iftikhar, “Network Forensic Analysis of TwitterApplication on Android OS,” Proc. - 2022 Int. Conf. Front. Inf. Technol. FIT 2022, pp. 249–254, 2022, doi: 10.1109/FIT57066.2022.00053.

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Published
2023-08-05
How to Cite
[1]
I. Sembiring, A. Iriani, and S. Suharyadi, “Exploring Alternative Approaches for TwitterForensics: Utilizing Social Network Analysis to Identify Key Actors and Potential Suspects”, intensif, vol. 7, no. 2, pp. 161-176, Aug. 2023.