用写作习惯识别网络贩子，帮警方连线索、破大案
Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers
- 通过分析广告文案和图片风格，追踪匿名账号的写作风格一致性
- 在非法线上市场中成功链接多个关联账号，发现重复作案模式
- 提出使用伦理指南，确保技术不侵犯隐私且公平透明
本研究探讨如何利用数据驱动的机器学习方法更好地理解并关联在线犯罪行为。大量非法活动如人口贩卖和违禁品交易已转移至线上平台，犯罪分子常使用匿名账号并频繁更换身份，使执法机构难以掌握其网络规模及账号间的关联。研究发现，即使试图隐藏身份，人们在撰写广告和呈现图片时仍会保持一致的表达模式。通过对大规模在线广告数据的分析，研究展示了如何识别相关账号并发现跨平台的重复行为。此外，研究还提出使用此类技术的伦理准则，强调在应用过程中必须尊重隐私、公平与透明。总体而言，该研究为执法调查提供了实用工具，并强调技术应用需谨慎、合规。
原文摘要 · Abstract (English)
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.
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