The Language of Legal and Illegal Activity on the Darknet

Leshem Choshen, Dan Eldad, Daniel Hershcovich, Elior Sulem, Omri Abend


Abstract
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well do off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drugs-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.
Anthology ID:
P19-1419
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4271–4279
Language:
URL:
https://aclanthology.org/P19-1419
DOI:
10.18653/v1/P19-1419
Bibkey:
Cite (ACL):
Leshem Choshen, Dan Eldad, Daniel Hershcovich, Elior Sulem, and Omri Abend. 2019. The Language of Legal and Illegal Activity on the Darknet. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4271–4279, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
The Language of Legal and Illegal Activity on the Darknet (Choshen et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P19-1419.pdf
Supplementary:
 P19-1419.Supplementary.zip
Presentation:
 P19-1419.Presentation.pdf
Video:
 https://vimeo.com/385198774
Code
 huji-nlp/cyber