The Dark Web presents a challenging and complex environment where cyber criminals conduct illicit activities with high degrees of anonymity and privacy. This chapter describes a honeypot-based data collection approach for Dark Web browsing that incorporates honeypots on three isolated virtual machines, including production honeypots, an onion-website-based research honeypot (Honey Onion) offering illegal services and a log server that collects and securely stores the honeypot logs. Experiments conducted over 14 days collected more than 250 requests on the Honey Onion service and in excess of 28,000 chat records from the Dark Web forum. The log server also monitored Honey Onion traffic, providing details such as packet types, timestamps, network data, and HTTP requests. The data collection results provide valuable insights into Dark Web activities, including malicious, benign and uncategorized activities. The data analysis identified common user categories such as malicious actors, researchers and security professionals, and uncategorized actors. The experimental results demonstrate that honeypot-based data collection can advance Dark Web investigations as well as enable the development of effective cyber security strategies and efforts to combat cyber crime in the Dark Web.