# URM4DMU: an user represention model for darknet markets users

arXiv: https://arxiv.org/abs/2303.10674 · HTML: https://mlsanity.com/paper/2303.10674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [Modeling Localness for Self-Attention Networks](https://mlsanity.com/paper/1810.10182/) (2018)
- [Convolutional Self-Attention Networks](https://mlsanity.com/paper/1904.03107/) (2019)
- [Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution](https://mlsanity.com/paper/1609.06686/) (2016)
- [Learning Invariant Representations of Social Media Users](https://mlsanity.com/paper/1910.04979/) (2019)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)
- [Content and popularity analysis of Tor hidden services](https://mlsanity.com/paper/1308.6768/) (2013)
- [Collective Dynamics of Dark Web Marketplaces](https://mlsanity.com/paper/1911.09536/) (2019)
- [SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis](https://mlsanity.com/paper/2104.00764/) (2021)

## Abstract (arXiv)

Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on different markets makes it easy to aggregate user information across different platforms, which helps identify anonymous users. Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts. While recent works mainly use CNN to model the text information of posts, failing to effectively model posts whose length changes frequently in an episode. To address the above problems, we propose a model named URM4DMU(User Representation Model for Darknet Markets Users) which mainly improves the post representation by augmenting convolutional operators and self-attention with an adaptive gate mechanism. It performs much better when combined with the temporal content and the forum interaction of posts. We demonstrate the effectiveness of URM4DMU on four darknet markets. The average improvements on MRR value and Recall@10 are 22.5% and 25.5% over the state-of-the-art method respectively.