Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers

TL;DR

This paper explores how machine learning-based authorship attribution can link online criminal profiles, aiding law enforcement in understanding and investigating illegal online activities while emphasizing ethical considerations.

Contribution

It introduces a method to analyze writing and presentation patterns for linking online profiles involved in illegal activities, with guidelines for responsible use.

Findings

Patterns in online advertisements are consistent enough for linking accounts.

Authorship attribution can effectively connect related online profiles.

The research emphasizes ethical guidelines for applying these methods.

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.