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

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5635–5649 Florence, Italy, July 28 - August 2, 2019. c⃝2019 Association for Computational Linguistics 5635 Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good Xuewei Wang∗1, Weiyan Shi∗2, Richard Kim2, Yoojung Oh2 Sijia Yang3, Jingwen Zhang2 and Zhou Yu2 1 Zhejiang University,2 University of California, Davis,3 University of Pennsylvania cheriewang@zju.edu.cn,{wyshi, khgkim, yjeoh}@ucdavis.edu, sijia.yang@asc.upenn.edu,{jwzzhang, joyu}@ucdavis.edu Abstract Developing intelligent persuasive conversa- tional agents to change people’s opinions and actions for social good is the frontier in ad- vancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strate- gic disclosures and appeals employed in hu- man persuasion conversations. We designed an online persuasion task where one partici- pant was asked to persuade the other to do- nate to a specific charity. We collected a large dataset with 1,017 dialogues and anno- tated 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. Fur- thermore, to develop an understanding of per- sonalized persuasion processes, we analyzed the relationships between individuals’ demo- graphic and psychological backgrounds in- cluding personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depend- ing on the individuals’ personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system. 1 1 Introduction Persuasion aims to use conversational and messag- ing strategies to change one specific person’s atti- tude or behavior. Moreover, personalized persua- sion combines both strategies and user informa- tion related to the outcome of interest to achieve better persuasion results (Kreuter et al., 1999; Rimer and Kreuter, 2006). Simply put, the goal of personalized persuasion is to produce desired * Equal contribution. 1The dataset and code are released at https:// gitlab.com/ucdavisnlp/persuasionforgood. changes by making the information personally rel- evant and appealing. However, two questions about personalized persuasion still remain unex- plored. First, we concern about how personal in- formation would affect persuasion outcomes. Sec- ond, we question about what strategies are more effective considering different user backgrounds and personalities. The past few years have witnessed the rapid development of conversational agents. The pri- mary goal of these agents is to facilitate task- completion and human-engagement in practi- cal contexts (Luger and Sellen, 2016; Bickmore et al., 2016; Graesser et al., 2014; Yu et al., 2016b). While persuasive technologies for behav- ior change have successfully leveraged other sys- tem features such as providing simulated experi- ences and behavior reminders (Orji and Moffatt, 2018; Fogg, 2002), the development of automated persuasive agents remains lagged due to the lack of synergy between the social scientific research on persuasion and the computational development of conversational systems. In this work, we introduced the foundation work on building an automatic personalized persuasive dialogue system. We first collected 1,017 …

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