Early detection of topical expertise in community question and answering

We focus on detecting potential topical experts in community question answering platforms early on in their lifecycle. We use a semi-supervised machine learning approach. We extract three types of feature: (i) textual, (ii) behavioral, and (iii) time-aware, which we use to predict whether a user will become an expert in the longterm. We compare our method to a machine learning method based on a state-of-the-art method in expertise retrieval. Results on data from Stack Overflow demonstrate the utility of adding behavioral and time-aware features to the baseline method with a net improvement in accuracy of 26% for very early detection of expertise.