The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments

Abstract

An audience's prior beliefs and morals are strong indicators of how likely they will be affected by a given argument. Utilizing such knowledge can help focus on shared values to bring disagreeing parties towards agreement. In argumentation technology, however, this is barely exploited so far. This paper studies the feasibility of automatically generating morally framed arguments as well as their effect on different audiences. Following the moral foundation theory, we propose a system that effectively generates arguments focusing on different morals. In an in-depth user study, we ask liberals and conservatives to evaluate the impact of these arguments. Our results suggest that, particularly when prior beliefs are challenged, an audience becomes more affected by morally framed arguments.

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“…et al [38], [39]. This corpus has been used to design novel methods for moral sentiment classification [40] used in models to investigate the impacts of moral framing in other domains (e.g., misinformation and polarization Mutlu et al , Ruch et al [41], [42] and has been applied to train models that produce morally salient text (e.g., arguments and jokes, Alshomary et al Yamane et al [43], [44])…”