Evolution of Deliberative Processes in Online Debates

Abstract

Deliberation on social networks is shaping the future of democratic processes and public discourse. With the increasing scale of social networks, it is however difficult to understand how deliberative processes work at scale and how they could be automatically optimised. Identifying deliberative processes in online debates and understanding their evolution could shed light on how quality-deliberation occurs and how to harness it using AI technologies. In this paper, we quantify deliberation in online debates and then propose a principled methodology to study their evolution. We start by looking at debates structured around issues, ideas, and arguments. We then analyse the time series of such utterances using natural language processing (NLP) and information theoretic methods. Finally, we conduct a social experiment to evaluate the method on an online debate. We show how the method identifies stable behaviors that reflect deliberative patterns in debates. We also show the role that non argumentative utterances have in creating feedback loops that characterise deliberative processes.