Shedding New Light on the Language of the Dark Web

Youngjin Jin, Eugene Jang, Yongjae Lee, Seungwon Shin, Jin-Woo Chung


Abstract
The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. Previous works on text classification of Dark Web domain have suggested that the use of deep neural models may be ineffective, potentially due to the linguistic differences between the Dark and Surface Webs. However, not much work has been done to uncover the linguistic characteristics of the Dark Web. This paper introduces CoDA, a publicly available Dark Web dataset consisting of 10000 web documents tailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web. We also assess the performance of various methods of Dark Web page classification. Finally, we compare CoDA with an existing public Dark Web dataset and evaluate their suitability for various use cases.
Anthology ID:
2022.naacl-main.412
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5621–5637
Language:
URL:
https://aclanthology.org/2022.naacl-main.412
DOI:
10.18653/v1/2022.naacl-main.412
Bibkey:
Cite (ACL):
Youngjin Jin, Eugene Jang, Yongjae Lee, Seungwon Shin, and Jin-Woo Chung. 2022. Shedding New Light on the Language of the Dark Web. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5621–5637, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Shedding New Light on the Language of the Dark Web (Jin et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.412.pdf
Software:
 2022.naacl-main.412.software.zip