Persuasion for Good:
Towards a Personalized Persuasive Dialogue System for Social Good
Abstract
Developing intelligent persuasive conversational agents to change people’s opinions and
actions for social good is the frontier in advancing the ethical development of automated
dialogue systems. To do so, the first step is to
understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed
an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a
large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a
subset. Based on the annotation, we built
a baseline classifier with context information
and sentence-level features to predict the 10
persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed
the relationships between individuals’ demographic and psychological backgrounds including personality, morality, value systems,
and their willingness for donation. Then, we
analyzed which types of persuasion strategies
led to a greater amount of donation depending on the individuals’ personal backgrounds.
This work lays the ground for developing a
personalized persuasive dialogue system