From Voice to Value: Leveraging AI to Enhance Spoken Online Reviews on the Go
| Autor: | Ravishan, Kavindu, Szabó, Dániel, van Berkel, Niels, Visuri, Aku, Yang, Chi-Lan, Yatani, Koji, Hosio, Simo |
|---|---|
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | International Conference on Mobile and Ubiquitous Multimedia MUM '24, December 1-4, 2024, Stockholm, Sweden |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3701571.3701593 |
| Popis: | Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems. Comment: \c{opyright} Kavindu Ravishan | ACM 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the Proceedings of the ACM Conference on Mobile and Ubiquitous Multimedia (MUM '24), http://dx.doi.org/10.1145/3701571.3701593 |
| Databáze: | arXiv |
| Externí odkaz: |
|