Abstract
Our research objective is to study the role of metaphor on the effectiveness of technologies that are designed to nudge people towards more healthy or socially appropriate behaviors. Towards this goal, we focus on the problem of motivating and encouraging appropriate social behaviors in the context of the ongoing COVID-19 pandemic, such as maintaining mandated social distance, wearing masks, and washing hands. Over the last two years, many countries have developed different approaches to promoting and enforcing the mandated behaviors. Here, we explore metaphor-based solutions to this problem by studying the following research questions: (1) How is it possible for artificial agents to recognize inappropriate behavior (mobile systems, robots)? (2) How to design metaphor-based interfaces of artificial agents that effectively influence relevant human decisions and choices in the event of improper behavior? Our approach is implemented in three steps: (1) Identifying inappropriate behaviors in the context of maintaining social distance. (2) Designing a persuasive metaphor-based interface to nudge people towards appropriate behaviors. (3) Designing a user study by deploying technologies that incorporate the interface. This research is interdisciplinary and concerns cognitive linguistics, IT, human-computer interactions, cognitive science, media ethics, and philosophy of law.
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Notes
- 1.
The WHO recommendation on physical distancing is available at https://www.who.int/westernpacific/emergencies/covid-19/information/physical-distancing (last accessed 2022/03/30).
- 2.
The #ReframeCovid Initiative and the collection of COVID-19 metaphors is available at https://sites.google.com/view/reframecovid/home (last accessed 2022/03/30).
- 3.
The video is available at https://www.youtube.com/watch?v=8Hi9-5F2zW4 (last accessed 2022/03/30).
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Ervas, F., Gunia, A., Lorini, G., Stojanov, G., Indurkhya, B. (2022). Fostering Safe Behaviors via Metaphor-Based Nudging Technologies. In: Cerone, A., et al. Software Engineering and Formal Methods. SEFM 2021 Collocated Workshops. SEFM 2021. Lecture Notes in Computer Science, vol 13230. Springer, Cham. https://doi.org/10.1007/978-3-031-12429-7_4
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