The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
This work addresses the challenge of improving argumentation technology for persuasion in contexts like politics or social debates, though it is incremental as it builds on existing moral foundation theory.
The paper tackled the problem of automatically generating morally framed arguments to influence audiences with different prior beliefs, finding that such arguments are more effective when challenging those beliefs.
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.