Multiple Case Study Validation of Online Social Networks Forensic Investigation Metamodel (OSNFIM)

Digital Forensic Investigation is facing complex challenges due to the proliferation of the online social networks (OSNs). This necessitates the development of robust frameworks to guide the whole investigation process. In order to address these challenges, a structured and comprehensive framework; the Online Social Networks Forensic Investigation Metamodel (OSNFIM) was developed. Also, to evaluate the applicability, dependability, performance, and efficiency of OSNFIM across retrospective documented investigations cases, multiple case study validation technique was applied to examine three distinctive and documented cases in this study, based on the four phases of the OSNFIM; Preliminary, Acquisition/Preservation, Analysis, and Presentation. The validation process ensures that each conceptual element is addressed through each phase by following the OSNFIM framework. The validation has demonstrated the applicability of the OSNFIM framework to various OSN-related forensic investigations. By validating a metamodel that improves investigative precision in the landscape of OSNs, this research has contributed to the rising field of digital forensics.

forensic investigation, online social networks, multiple case study, metamodel validation

1. Sethurajan, M. R. and Natarajan K. A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model. Discov. Comput. 2025;1–33. [Google Scholar] [Crossref]

2. Bade A. M. and Othman SH. Towards Adapting Metamodelling Technique for an Online Social Networks Forensic Investigation (OSNFI) Domain. Int. J. Adv. Comput. Sci. Appl. 2022;13:166–73. [Google Scholar] [Crossref]

3. Cibrián E, Olivert-iserte J, Díez-fenoy C, Llorens J, Álvarez-rodríguez JM. Ensuring Semantic Consistency in SysML v2 Models Through Metamodel-Driven Validation. 2025;13. [Google Scholar] [Crossref]

4. Celio, E., Brunner, S. H., & Grêt-regamey, A. (2012). Participatory Land Use Modeling with Bayesian Networks : a Focus on Subjective Validation Participatory Land Use Modeling with Bayesian Networks : a Focus on Subjective Validation. [Google Scholar] [Crossref]

5. Ali A, Abd Razak S, Othman SH, Mohammed A, Saeed F (2017) A metamodel for mobile forensics investigation domain. PLoS ONE 12(4): e0176223. doi:10.1371/journal.pone.0176223 [Google Scholar] [Crossref]

6. IEEE. (2010). IEEE Recommended Practice for Distributed Interactive Simulation-Verification, Validation, and Accreditation. [Google Scholar] [Crossref]

7. Ralyté J, Koutsopoulos G, Stirna J. Verification , validation , and evaluation of modeling methods : experiences and recommendations. Softw. Syst. Model. [Internet] 2025;Available from: https://doi.org/10.1007/s10270-025-01304-2 [Google Scholar] [Crossref]

8. Xiang, X., Kennedy, R. and Madey, G. (2005) ‘Verification and Validation of Agent based Scientific Simulation Models’, Agent-Directed Simulation Conference, pp. 47–55. Available at: http://www.nd.edu/~nom/Papers/ADS019_Xiang.pdf [Google Scholar] [Crossref]

9. Sargent, R. G. (2013) ‘Verification and validation of simulation models’, Journal of Simulation, 7(1), pp. 12–24. doi: 10.1057/jos.2012.20. [Google Scholar] [Crossref]

10. Bade, A. M. et al. (2023) ‘Expert Validation of Online Social Networks Forensic Investigation Metamodel (OSNFIM)’, 2023 International Conference on Data Science and Its Applications, ICoDSA 2023, pp. 500–505. doi: 10.1109/ICoDSA58501.2023.10277048. [Google Scholar] [Crossref]

11. Fernández-Gómez, E. et al. (2020) ‘Content validation through expert judgement of an instrument on the nutritional knowledge, beliefs, and habits of pregnant women’, Nutrients, 12(4). doi: 10.3390/nu12041136. [Google Scholar] [Crossref]

12. Bade, A. M. and Othman, S. H. (2021) ‘A Systematic Review of Published Articles, Phases and Activities in an Online Social Networks Forensic Investigation Domain’, International Journal of Advanced Computer Science and Applications, 12(11), pp. 153–160. doi: 10.14569/IJACSA.2021.0121118. [Google Scholar] [Crossref]

13. Arshad, H., Jantan, A., & Omolara, E. (2019). Evidence collection and forensics on social networks: Research challenges and directions. Digital Investigation, 28, 126–138. https://doi.org/10.1016/j.diin.2019.02.001 [Google Scholar] [Crossref]

14. Taylor, D. C. P. J. et al. (2019) ‘Science & Justice Forensic investigation of cross platform massively multiplayer online games : Minecraft as a case study’, Science & Justice, 59(3), pp. 337–348. doi: 10.1016/j.scijus.2019.01.005. [Google Scholar] [Crossref]

15. Arshad, H., Omlara, E., Oludare, I., & Aminu, A. (2020). Computers & Security A semi-automated forensic investigation model for online social networks. Computers & Security, 97, 101946. https://doi.org/10.1016/j.cose.2020.101946 [Google Scholar] [Crossref]

16. Mohd Zainudin, N., Merabti, M., & Llewellyn-Jones, D. (2011). Online social networks as supporting evidence: A digital forensic investigation model and its application design. 2011 International Conference on Research and Innovation in Information Systems, ICRIIS’11. https://doi.org/10.1109/ICRIIS.2011.6125728 [Google Scholar] [Crossref]

17. Zainudin et al. (2010) ‘A Digital Forensic Investigation Model for Online Social Networking’, pp. 1–6. [Google Scholar] [Crossref]

18. Vickery, J. (2008). The Megan Meier MySpace suicide: A case study exploring the social aspects of convergent media, citizen journalism, and online anonymity and credibility. International Symposium on Online Journalism. Retrieved from https://isoj.org/wp-content/uploads/2018/01/Jaqueline.pdf [Google Scholar] [Crossref]

19. United States Department of Justice. (2021, July 27). Three individuals charged for alleged roles in Twitter hack. Retrieved from https://www.justice.gov/opa/pr/three-individuals-charged-alleged-roles-twitter-hack [Google Scholar] [Crossref]

20. Sharma, U. and Kalekar, S. M. (2024). Dissecting the Uber security breach: Root cause analysis and mitigation strategies. International Journal of Computer Engineering and Technology, 15(4), 620–628. Retrieved December 30, 2025, from https://lib index.com/index.php/IJCET/article/view/IJCET_15_04_062 [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet