This paper introduces a novel user classification task in the context of web user forums. We present a definition of four basic user characteristics and an annotated dataset. We outline a series of approaches for predicting user characteristics, utilising aggregated post features and user/thread network analysis in a supervised learning context. Using the proposed feature sets, we achieve results above both a naive baseline and a bag-ofwords approach, for all four of our basic user characteristics. In all cases, our bestperforming classifier is statistically indistinct from an upper bound based on the inter-annotator agreement for the task.
REF introduced a novel user classification task where each user is classified against four attributes: clarity, proficiency, positivity and effort.
Classifying User Forum Participants: Separating the Gurus from the Hacks, and Other Tales of the Internet
ALTA
Computer Science