Inferring stance in news broadcasts from prosodic-feature configurations

Speech conveys many things beyond content, including aspects of appraisal, feeling, and attitude that have not been much studied. In this work, we identify 14 aspects of stance that occur frequently in radio news stories and that could be useful for information retrieval, including indications of subjectivity, immediacy, local relevance, and newness. We observe that newsreaders often mark their stance with prosody. To model this, we treat each news story as a collection of overlapping 6-s patches, each of which may convey one or more aspects of stance by its prosody. The stance of a story is then estimated from the information in its patches. Experiments with English, Mandarin, and Turkish show that this technique enables automatic identification of many aspects of stance in news broadcasts.