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Abstract. This paper investigates graph-based approaches to labeled topic clustering of reader comments in online news. For graph-based clustering we propose a linear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract topics extracted from the clusters. We evaluate the clustering approach against gold standard data created by human annotators and compare its results against LDA -currently reported as the best method for the news comment clustering task. Evaluation of cluster labelling is set up as a retrieval task, where human annotators are asked to identify the best cluster given a cluster label. Our clustering approach significantly outperforms the LDA baseline and our evaluation of abstract cluster labels shows that graph-based approaches are a promising method of creating labeled clusters of news comments, although we still find cases where the automatically generated abstractive labels are insufficient to allow humans to correctly associate a label with its cluster.

IntroductionSurveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: “experiences”, “facts” and “opinions”, using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers.Material and methodsWe work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as “experience”, “fact” or “opinion”. Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure.ResultsOverall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results.

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news. To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult. In this paper we propose one type of reader comment summary -the conversation overview summary -that aims to capture the key argumentative content of a reader comment conversation. We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpusthe first of its kind -of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.

Overlapping talk is common in talk-in-interaction. Much of the previous research on this topic agrees that speaker overlaps can be either turn competitive or noncompetitive. An investigation of the differences in prosodic design between these two classes of overlaps can offer insight into how speakers use and orient to prosody as a resource for turn competition.In this paper, we investigate the role of fundamental frequency (F 0 ) as a resource for turn competition in overlapping speech. Our methodological approach combines detailed conversation analysis of overlap instances with acoustic measurements of F 0 in the overlapping sequence and in its local context. The analyses are based on a collection of overlap instances drawn from the ICSI Meeting corpus. We found that overlappers mark an overlapping incoming as competitive by raising F 0 above their norm for turn beginnings, and retaining this higher F 0 until the point of overlap resolution. Overlappees may respond to these competitive incomings by returning competition, in which case they raise their F 0 too. Our results thus provide instrumental support for earlier claims made on impressionistic evidence, namely that participants in talk-in-interaction systematically manipulate F 0 height when competing for the turn.