System Pulls Answers From Online Conversations By Identifying The Alpha Chatterers

A system pulls answers from online conversations by identifying the alpha chatterers. The system was developed to analyze technical conversations in which an objectively correct answer exists. Online communities are now firmly established in domains ranging from high school gossip to professional open-source software design discussions, generating huge repositories of records of human knowledge processing, pre-converted to digital form. "For study of online natural language interaction, it's the mother lode." The fact that human conversation has an inherent structure, including temporal ordering, references to previous statements, labeled sourcing and other clues opens the door to much deeper machine-generated understanding. To make use of the structure, the team used a graph-based algorithm called HITS (Hypertext Induced Topic Selection). The interactions used in the study were threaded discussions from three semesters of an undergraduate course in computer science, including 2214 messages in 640 threads, all discussing class material and posing questions about problems. The implementation integrates three separate elements--speech act analysis, lexical similarity, and poster trustworthiness--to create links for interpretation for individual conversation participants.