[2605.04080] Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers
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Computer Science > Computation and Language
arXiv:2605.04080 (cs)
[Submitted on 14 Apr 2026]
Title:Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers
Authors:Vageesh Kumar Saxena
View a PDF of the paper titled Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers, by Vageesh Kumar Saxena
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Abstract:This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illegal activities, such as human trafficking and illicit trade, have moved to online platforms where offenders hide behind anonymous accounts and frequently change identities. This makes it difficult for authorities to understand how large these networks are and how different online profiles may be linked.
The research shows that people tend to maintain consistent patterns in how they write advertisements and present images online, even when they try to stay anonymous. By analysing these patterns across large collections of online advertisements, the research demonstrates how to link related accounts and identify repeated behaviour across illegal online markets.
In addition, the research also addresses how such methods should be used responsibly. It proposes clear guidelines to ensure that privacy, fairness, and transparency are respected when these tools are applied. Overall, the research provides practical ways to support law enforcement investigations while emphasising careful and ethical use.
Comments: Doctoral thesis
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
Cite as: arXiv:2605.04080 [cs.CL]
(or arXiv:2605.04080v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.04080
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arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.26481/dis.20250107vs
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DOI(s) linking to related resources
Submission history
From: Vageesh Saxena [view email] [v1] Tue, 14 Apr 2026 19:34:13 UTC (26,256 KB)
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