# Shedding New Light on the Language of the Dark Web

arXiv: https://arxiv.org/abs/2204.06885 · HTML: https://mlsanity.com/paper/2204.06885/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 14 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [The Dark Side of the Language: Pre-trained Transformers in the DarkNet](https://mlsanity.com/paper/2201.05613/) (2022)
- [The Language of Legal and Illegal Activity on the Darknet](https://mlsanity.com/paper/1905.05543/) (2019)
- [Content and popularity analysis of Tor hidden services](https://mlsanity.com/paper/1308.6768/) (2013)
- [Structure and Content of the Visible Darknet](https://mlsanity.com/paper/1811.01348/) (2018)

## Built on by (most cited first, 5 of 5)

- [DarkBERT: A Language Model for the Dark Side of the Internet](https://mlsanity.com/paper/2305.08596/) (2023)
- [VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets](https://mlsanity.com/paper/2305.02763/) (2023)
- [Two-step Automated Cybercrime Coded Word Detection using Multi-level Representation Learning](https://mlsanity.com/paper/2403.10838/) (2024)
- [Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection](https://mlsanity.com/paper/2503.14926/) (2025)
- [A Language Model-Driven Semi-Supervised Ensemble Framework for Illicit Market Detection Across Deep/Dark Web and Social Platforms](https://mlsanity.com/paper/2507.22912/) (2025)

## Abstract (arXiv)

The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. Previous works on text classification of Dark Web domain have suggested that the use of deep neural models may be ineffective, potentially due to the linguistic differences between the Dark and Surface Webs. However, not much work has been done to uncover the linguistic characteristics of the Dark Web. This paper introduces CoDA, a publicly available Dark Web dataset consisting of 10000 web documents tailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web. We also assess the performance of various methods of Dark Web page classification. Finally, we compare CoDA with an existing public Dark Web dataset and evaluate their suitability for various use cases.