# The Language of Legal and Illegal Activity on the Darknet

arXiv: https://arxiv.org/abs/1905.05543 · HTML: https://mlsanity.com/paper/1905.05543/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 7 of 7)

- [DarkBERT: A Language Model for the Dark Side of the Internet](https://mlsanity.com/paper/2305.08596/) (2023)
- [The Dark Side of the Language: Pre-trained Transformers in the DarkNet](https://mlsanity.com/paper/2201.05613/) (2022)
- [Shedding New Light on the Language of the Dark Web](https://mlsanity.com/paper/2204.06885/) (2022)
- [VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets](https://mlsanity.com/paper/2305.02763/) (2023)
- [MATCHED: Multimodal Authorship-Attribution To Combat Human Trafficking in Escort-Advertisement Data](https://mlsanity.com/paper/2412.13794/) (2024)
- [When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning](https://mlsanity.com/paper/2311.17026/) (2023)
- [Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning](https://mlsanity.com/paper/2403.10838/) (2024)

## Abstract (arXiv)

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drug-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.