Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good

TL;DR

This paper tackles building personalized persuasive dialogue systems for social good by examining how individual differences shape persuasion. It collects PersuasionForGood, a dataset of 1,017 human-human dialogues, annotated with 10 persuasion strategies and donor outcomes. A hybrid RCNN classifier using sentence, context, and sentence-level features predicts strategies, while the authors analyze how demographics and personality traits relate to donation and strategy effectiveness. Results show Donation information is particularly effective and reveal heterogeneity in strategy effects across traits, offering a path toward adaptive, ethically informed persuasive agents.

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.

Figures (7)

• Figure 1: Distributions of the seven persuasive appeals across turns.
• Figure 2: Distributions of the three persuasive inquiries across turns.
• Figure 3: The hybrid RCNN model combines sentence embedding, context embedding and sentence-level features. "+" represents vector concatenation. The blue dotted box shows the sentence embedding part. The orange dotted box shows the context embedding part. The green dotted box shows the sentence-level features.
• Figure 4: Big-Five traits score distribution for people who donated and didn't donate. For all the 471 persuadees who did not donate in the PersuasionForGood, we compared their personalities score with the other 546 persuadees who donated. The result shows that people who donated have a higher score on agreeableness and openness in the Big-Five analysis. Because strategy annotation was not involved in the psychological analysis, we used the whole dataset (1017 dialogues) for this analysis.
• Figure 5: Confusion matrix for the ten persuasion strategies and the non-strategy category on the AnnSet using the hybrid RCNN model with all the features.