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
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 drug-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.

本研究通过将毒品相关网站作为测试案例，比较在深网和表面网络中合法和非法毒品销售文本的语言特征，发现它们在部分词性标注的分配和维基百科中命名实体的文本覆盖率方面存在明显差异。