  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.
