Web usage mining has been much concentrated on the discovery of relevant user behaviours from Web access record data. In this paper, we present WebUser, an approach to discover unexpected usage in Web access log. We present a belief-driven method for extracting unexpected Web usage sequences, where the belief system consists of a temporal relation and semantics constrained sequence rules acquired with respect to prior knowledge. Our experiments show the effectiveness and usefulness of the proposed approach. Further, discovered rules of unexpected Web usage can be used for Web content personalisation and recommendation, site structure optimisation, and critical event prediction.