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The hidden nature and the limited accessibility of the Dark Web, combinedwith the lack of public datasets in this domain, make it difficult to study itsinherent characteristics such as linguistic properties. Previous works on textclassification of Dark Web domain have suggested that the use of deep neuralmodels may be ineffective, potentially due to the linguistic differencesbetween the Dark and Surface Webs. However, not much work has been done touncover the linguistic characteristics of the Dark Web. This paper introducesCoDA, a publicly available Dark Web dataset consisting of 10000 web documentstailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct athorough linguistic analysis of the Dark Web and examine the textualdifferences between the Dark Web and the Surface Web. We also assess theperformance of various methods of Dark Web page classification. Finally, wecompare CoDA with an existing public Dark Web dataset and evaluate theirsuitability for various use cases.

Shedding New Light on the Language of the Dark Web