Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
This work addresses the challenge of creating personalized persuasive agents for social good, but it is incremental as it focuses on foundational data collection and analysis rather than a deployed system.
The study tackled the problem of understanding human persuasion strategies in conversations to develop ethical persuasive dialogue systems for social good, resulting in a dataset of 1,017 dialogues and a baseline classifier predicting 10 persuasion strategies with analysis linking personal backgrounds to donation outcomes.
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.