Analysts populated data from thirty-five Chinese hacker website into a graph database management systems to depict risk relevant chatter from between January 2006 and May 2019—substantive discussions around threat types suggesting current or future attack possibility—in the dataset. Analysts are able to identify specific threat actors and groups through graph queries using keyword searches. By traversing the graph and expanding the relationships among threat actors through shared posts, analysts can quantify the impact of this chatter—how related tools, tactics and techniques are shared, discussed and transferred—on the cyber crime threat landscape. This research introduces the methodology and provides a breakdown of the volume of data according to threat type. It delves into specific threat types and demonstrates how graphs facilitate deep and dark web (DDW) data analysis and intelligence generation.