# Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information

arXiv: https://arxiv.org/abs/2312.04944 · HTML: https://mlsanity.com/paper/2312.04944/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs](https://mlsanity.com/paper/2505.02009/) (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)
- [Detection of Illicit Content on Online Marketplaces using Large Language Models](https://mlsanity.com/paper/2603.04707/) (2026)

## Abstract (arXiv)

This work aims at expanding previous works done in the context of illegal activities classification, performing three different steps. First, we created a heterogeneous dataset of 113995 onion sites and dark marketplaces. Then, we compared pre-trained transferable models, i.e., ULMFit (Universal Language Model Fine-tuning), Bert (Bidirectional Encoder Representations from Transformers), and RoBERTa (Robustly optimized BERT approach) with a traditional text classification approach like LSTM (Long short-term memory) neural networks. Finally, we developed two illegal activities classification approaches, one for illicit content on the Dark Web and one for identifying the specific types of drugs. Results show that Bert obtained the best approach, classifying the dark web's general content and the types of Drugs with 96.08% and 91.98% of accuracy.