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Article
A Broad Evaluation of the Tor English Content Ecosystem
WebSci '19: Proceedings of the 10th ACM Conference on Web Science
  • Mahdieh Zabihimayvan, Wright State University
  • Reza Sadeghi, Wright State University
  • Derek Doran, Wright State University - Main Campus
  • Mehdi Allahyari
Document Type
Article
Publication Date
6-1-2019
Disciplines
Abstract

Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and as a platform to market illicit goods and data. Past studies on the content of Tor support this notion, but were carried out by targeting popular domains likely to contain illicit content. A survey of past studies may thus not yield a complete evaluation of the content and use of Tor. This work addresses this gap by presenting a broad evaluation of the content of the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web and, through topic and network analysis, characterize the types of information and services hosted across a broad swath of Tor domains and their hyperlink relational structure. We recover nine domain types defined by the information or service they host and, among other findings, unveil how some types of domains intentionally silo themselves from the rest of Tor. We also present measurements that (regrettably) suggest how marketplaces of illegal drugs and services do emerge as the dominant type of Tor domain. Our study is the product of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a collection of over 150,000 Tor pages. We make a dataset of the intend to make the domain structure publicly available as a dataset at this https URL.

DOI
https://doi.org/10.1145/3292522.3326031
Citation Information
Mahdieh Zabihimayvan, Reza Sadeghi, Derek Doran and Mehdi Allahyari. "A Broad Evaluation of the Tor English Content Ecosystem" WebSci '19: Proceedings of the 10th ACM Conference on Web Science (2019) p. 333 - 342
Available at: http://works.bepress.com/derek_doran/59/