Exploring chatbot user interfaces for mood measurement: a study of validity and user experience

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI:10.1145/3410530.3414395

Helma Torkamaan, J. Ziegler

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引用次数: 3

Abstract

With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user's mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools.

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探索聊天机器人用户界面的情绪测量:有效性和用户体验的研究

随着交互式文本或语音系统(如智能个人助理和聊天机器人)的发展，现在可以通过基于对话的互动而不是传统的问卷调查来轻松测量用户的情绪。然而，目前尚不清楚这种情绪测量是否有效，类似于传统的测量方法，以及用户粘性。使用智能手机，我们在本文中比较了两种最流行的传统情绪测量方法:国际PANAS-Short Form (I-PANAS-SF)和Affect Grid。对于每一种测量方法，我们用一个改进的、基于聊天机器人的用户界面设计来研究情绪测量的有效性。我们的初步结果表明，一些情绪测量方法可能无法适应修改，它们的改变可能导致无效的结果，如果不是无意义的结果。这篇探索性论文随后提出并讨论了四种基于语音的情绪追踪器设计，并总结了用户对这些工具的感知和满意度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

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