A natural way to resolve different points of view and form opinions is through exchanging arguments and knowledge. Facing the vast amount of available information on the internet, people tend to focus on information consistent with their beliefs. Especially when the issue is controversial, information is often selected that does not challenge one's beliefs. To support a fair and unbiased opinion-building process, we propose a chatbot system that engages in a deliberative dialogue with a human. In contrast to persuasive systems, the envisioned chatbot aims to provide a diverse and representative overview - embedded in a conversation with the user. To account for a reflective and unbiased exploration of the topic, we enable the system to intervene if the user is too focused on their pre-existing opinion. Therefore we propose a model to estimate the users' reflective engagement (RUE), defined as their critical thinking and open-mindedness. We report on a user study with 58 participants to test our model and the effect of the intervention mechanism, discuss the implications of the results, and present perspectives for future work. The results show a significant effect on both user reflection and total user focus, proving our proposed approach's validity.

翻译：解决不同观点并形成意见的自然途径是通过交流论点和知识。面对互联网上海量的信息，人们倾向于关注与其信念一致的信息，尤其是在争议性议题中，常会筛选不会挑战自身信念的信息。为支持公平公正的意见形成过程，我们提出一种与人进行审慎对话的聊天机器人系统。与说服性系统不同，该设想中的聊天机器人旨在提供多样化且具代表性的观点概览——嵌入与用户的对话中。为促进对议题的反思性且无偏见的探索，当用户过度聚焦于既有观点时，系统可进行干预。为此，我们提出一个模型来估算用户的反思性参与度（RUE），即其批判性思维与开放心态的程度。我们报告了基于58名参与者的用户研究，以测试模型及干预机制的效果，讨论结果的意义，并呈现未来工作展望。结果显示，该方案对用户反思程度和总体关注焦点均有显著影响，验证了所提方法的有效性。