What makes discussions constructive? : modeling argument and deliberative quality

Abstract

Public discourse is an important building block of democracy: it is where opinions are exchanged and certain narratives are formed or reinforced. In recent years, the importance of everyday discussions, for example on social media, has grown. These platforms allow citizens from different backgrounds and with different political views to participate. The aim of the following thesis is to empirically model constructive discussion behavior. Only constructive discourse can strengthen democracy through participation and the exchange of different perspectives. If this fails, the result is fake news and declining trust in democracy—and thus a threat to any democratic system. This work takes an interdisciplinary perspective. The research is based on actual discussions between lay citizens (e.g., online discussions). The theoretical basis is provided by normative frameworks that define what constitutes a constructive discussion or a good argument (e.g., deliberative theory or the definition of good argumentation in rhetoric). These are modeled and evaluated using methods from natural language processing (NLP). NLP allows large amounts of data to be analyzed efficiently—for example, as shown in a publication of this work, all discussion posts on Reddit about the COVID-19 pandemic. The resulting models can in turn be used to develop semi-automatic interventions for more constructive discussion behavior: the moderation of (online) discussions. The thesis is divided into three parts: the first part examines how central aspects of constructive discussion behavior can be defined and how they can be automatically measured or evaluated in natural language. In the social sciences, these aspects are summarized and characterized under the core concept of "deliberative quality", while in rhetoric and computational linguistics they are treated under the concept of "argument quality". The second part of this thesis takes a closer look at a particular aspect of deliberative quality: the narration of personal experiences and stories (known as "storytelling"), a phenomenon that is often used to clarify a point of view. In the context of constructive discourse, storytelling can, for example, promote perspective-taking, is less conflict-laden than value-based argumentation, and can contribute to creating a collective identity or a shared narrative. The third part of this thesis deals with semi-automatic support for moderation. This is based on data containing human moderation behavior (e.g., from experts in the field of moderation or from active discussion participants). In this part, the findings from the first two building blocks are brought together to examine the relationship between moderation and aspects of (non-)constructive discussion behavior and, based on this, how these insights can be used to develop models for supporting human moderators.