# Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation

arXiv: https://arxiv.org/abs/1708.09609 · HTML: https://mlsanity.com/paper/1708.09609/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 4 of 4)

- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [Euphemistic Phrase Detection by Masked Language Model](https://mlsanity.com/paper/2109.04666/) (2021)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Impromptu Cybercrime Euphemism Detection](https://mlsanity.com/paper/2412.01413/) (2024)

## Abstract (arXiv)

One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own "fine-grained domain" in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.