# SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis

arXiv: https://arxiv.org/abs/2104.00764 · HTML: https://mlsanity.com/paper/2104.00764/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [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)
- [Multi-task learning for Joint Language Understanding and Dialogue State Tracking](https://mlsanity.com/paper/1811.05408/) (2018)
- [TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook](https://mlsanity.com/paper/2002.07917/) (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)
- [Unsupervised Natural Question Answering with a Small Model](https://mlsanity.com/paper/1911.08340/) (2019)

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

- [VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web](https://mlsanity.com/paper/2207.03477/) (2022)
- [NBC-Softmax : Darkweb Author fingerprinting and migration tracking](https://mlsanity.com/paper/2212.08184/) (2022)
- [URM4DMU: an user represention model for darknet markets users](https://mlsanity.com/paper/2303.10674/) (2023)
- [Per-sender neural network classifiers for email authorship validation](https://mlsanity.com/paper/2509.00005/) (2025)

## Abstract (arXiv)

Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and location tracking, making it challenging to link across malicious users using multiple accounts (sybils). Additionally, users migrate to new forums when one is closed, making it difficult to link users across multiple forums. We develop a novel stylometry-based multitask learning approach for natural language and interaction modeling using graph embeddings to construct low-dimensional representations of short episodes of user activity for authorship attribution. We provide a comprehensive evaluation of our methods across four different darknet forums demonstrating its efficacy over the state-of-the-art, with a lift of up to 2.5X on Mean Retrieval Rank and 2X on Recall@10.