Muhammad Arshad
Ashfaq Ahmad
Choo Wou Onn
Emmanuel Arko Sam- 1School of Informatics and Cybersecurity, Technological University Dublin, Dublin, Ireland
- 2Faculty of Basic Sciences, Lahore Garrison University, Lahore, Pakistan
- 3Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
- 4Unicaf, Larnaca, Cyprus
- 5University of East London, London, United Kingdom
A Correction on
Investigating methods for forensic analysis of social media data to support criminal investigations
by Arshad, M., Ahmad, A., Onn, C. W., and Sam, E. A. (2025). Front. Comput. Sci. 7:1566513. doi: 10.3389/fcomp.2025.1566513
The below references for were erroneously written as:
Smith, R. & Patel, T. (2023). “Cross-Border Data Access in Digital Forensics”. Digital Investigation, 45, 101678.
Zhang, Y., et al. (2023) “Secure Federated Learning for Digital Forensics: A Provable Framework with Differential Privacy.” IEEE Transactions on Information Forensics and Security, 18, 4503-4517.
Liu, Y., et al. (2023). “Adversarial Validation for Bias Mitigation in Forensic Machine Learning”. IEEE Transactions on Information Forensics and Security, 18, 2105-2118.
The correct references are:
Zuo, Z. (2024). Cross-border data forensics: challenges and strategies in the belt and road initiative digital era. Editor. Board 20:49. doi: 10.5539/ass.v20n2p49
Zhang, Z., Wu, L., Ma, C., Li, J., Wang, J., Wang, Q., et al. (2022). LSFL: a lightweight and secure federated learning scheme for edge computing. IEEE Trans. Inf. Forensics Secur. 18, 365–379. doi: 10.1109/TIFS.2023.3331274
Pagano, T. P., Loureiro, R. B., Lisboa, F. V. N., Peixoto, R. M., Guimarães, G. A. S., Cruz, G. O. R., et al. (2023). Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Big Data Cogn. Comput. 7:15. doi: 10.3390/bdcc7010015
Zuo (2024) was not cited in the article. The citation has now been inserted into section 3.2 Data collection, 3.2.3 Ethical and legal compliance, First Paragraph and should read:
“The data collection strictly adhered to privacy laws such as GDPR and country jurisdiction guidelines. Where necessary, legal warrants or subpoenas were acquired to access restricted or private data. Kerr's (2022) seminal work on Computer Crime Law establishes the foundational standards for lawful acquisition of social media data, emphasizing chain of custody protocols that informed our blockchain-based preservation system (Section 6.2). For jurisdiction challenges, we reference the Zuo (2024) empirical study in Digital Investigation, which evaluates GDPR/CCPA compliance in 200+ cross-border cases, directly supporting our warrant-based data access procedures.”
Zhang et al. (2022) and Pagano et al. (2023) were not cited in the article. The citations have now been inserted in section 5.4 Bias, fairness, and responsible AI Second Paragraph and should read:
“While federated learning architectures show promise for privacy-preserving forensics, we prioritise peer-validated methods such as those formalized by Zhang et al. (2022) in their IEEE Transactions on Information Forensics and Security study, which demonstrated provable security guarantees for distributed forensic analysis while maintaining GDPR compliance. For adversarial robustness testing, we cite Pagano et al. (2023) an MDPI study, which formalizes bias-mitigation frameworks for forensic AI—an approach mirrored in our SHAP analysis.”
The original version of this article has been updated.
Publisher's note
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Keywords: AI in forensics, cybercrime investigation, food security, forensic analysis, gender injustices, social media forensics
Citation: Arshad M, Ahmad A, Onn CW and Sam EA (2025) Correction: Investigating methods for forensic analysis of social media data to support criminal investigations. Front. Comput. Sci. 7:1673393. doi: 10.3389/fcomp.2025.1673393
Received: 25 July 2025; Accepted: 28 October 2025;
Published: 17 November 2025.
Edited and reviewed by: Stelvio Cimato, University of Milan, Italy
Copyright © 2025 Arshad, Ahmad, Onn and Sam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Muhammad Arshad, TXVoYW1tYWQuYXJzaGFkQHR1ZHVibGluLmll