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date: 21 May 2019

Abstract and Keywords

Online communication is often characterized as dominated by antagonism or groupthink, with little in the way of meaningful interaction or persuasion. This essay examines how one can detect and measure instances of more productive conversation online, considered through the lens of deliberative theory. It begins with an examination of traditional deliberative democracy, then explores how these concepts have been applied to online deliberation and by those studying interpersonal conversation in social media more generally. These efforts to characterize and measure deliberative quality have resulted in a myriad of criteria, with elaborate checklists that are often as superficial as they are complex. This essay instead proposes targeting what is arguably the core deliberative process—a mutual consideration of conceptually interrelated ideas—in order to distinguish the better from the worse and to construct better conceptual structures. The essay finishes by discussing two computational models of argument quality and interdependence as templates for richer, scalable, nonpartisan measures of deliberative discussion online.

Keywords: social media, deliberation, interpersonal communication, debate, persuasion, natural language processing, argument mining, machine learning, network analysis, collective decisions

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