Identification of useful user comments in social media

Cultural institutions are increasingly opening up their repositories and contribute digital objects to social media platforms such as Flickr. In return they often receive user comments containing information that could be incorporated in their catalog records. Since judging the usefulness of a large number of user comments is a labor-intensive task, our aim is to provide automated support for filtering potentially useful social media comments on digital objects. In this paper, we discuss the notion of usefulness in the context of social media comments and compare it from end-users as well as expertusers perspectives. Then we present a machine-learning approach to automatically classify comments according to their usefulness. Our approach makes use of syntactic and semantic comment features and also considers user context. We present the results of an experiment we did on user comments received in six different Flickr Commons collections. They show that a few relatively straightforward features can be used to infer useful comments with up to 89% accuracy.