# Scraping the Shadows: Deep Learning Breakthroughs in Dark Web Intelligence

arXiv: https://arxiv.org/abs/2504.02872 · HTML: https://mlsanity.com/paper/2504.02872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://mlsanity.com/paper/2311.08526/) (2023)
- [A Comprehensive Evaluation Framework for Deep Model Robustness](https://mlsanity.com/paper/2101.09617/) (2021)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [Information Extraction in Illicit Domains](https://mlsanity.com/paper/1703.03097/) (2017)
- [The Dark Web Phenomenon: A Review and Research Agenda](https://mlsanity.com/paper/2104.07138/) (2021)

## Abstract (arXiv)

Darknet markets (DNMs) facilitate the trade of illegal goods on a global scale. Gathering data on DNMs is critical to ensuring law enforcement agencies can effectively combat crime. Manually extracting data from DNMs is an error-prone and time-consuming task. Aiming to automate this process we develop a framework for extracting data from DNMs and evaluate the application of three state-of-the-art Named Entity Recognition (NER) models, ELMo-BiLSTM citepShahEtAl2022, UniversalNER citepZhouEtAl2024, and GLiNER citepZaratianaEtAl2023, at the task of extracting complex entities from DNM product listing pages. We propose a new annotated dataset, which we use to train, fine-tune, and evaluate the models. Our findings show that state-of-the-art NER models perform well in information extraction from DNMs, achieving 91% Precision, 96% Recall, and an F1 score of 94%. In addition, fine-tuning enhances model performance, with UniversalNER achieving the best performance.