As liberal states across the world face a decline in political participation by citizens, deliberative democracy is a promising solution for the publics decreasing confidence and apathy towards the democratic process (Dahl et al., 2017). Deliberative dialogue is method of public interaction that is fundamental to the concept of deliberative democracy. The ability to identify and predict consensus in the dialogues could bring greater accessibility and transparency to the face-to-face participatory process. The paper sets out a research plan for the first steps at automatically identifying and predicting consensus in a corpus of German language debates on hydraulic fracking. It proposes the use of a unique combination of lexical, sentiment, durational and further derivative features of adjacency pairs to train traditional classification models. In addition to this, the use of deep learning techniques to improve the accuracy of the classification and prediction tasks is also discussed. Preliminary results at the classification of utterances are also presented, with an Fl between 0.61 and 0.64 demonstrating that the task of recognising agreement is demanding but possible.
展开▼
