  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.
