Abstract
Although computers could offer emotional support as well as task support when aiding a user for a complex task, there is little current understanding of how they might do this. Moreover existing demonstrations of emotional support, though promising, only cover a small number of types of support and investigate a limited number of algorithms designed by hand. In this paper, we present an empirical investigation that starts from first principles, determining different categories of stressors for which emotional support might be useful, different categories of emotional support utterances and promising algorithms for deciding the content and form of textual emotional support messages according to the stressors present. At each stage, the results are validated through empirical experiments with human participants who, for instance, are required to place statements into categories, evaluate possible support messages in different imagined situations and compose their own emotional support from options offered. This development methodology allows us to avoid potentially challenging ethical issues in presenting people with stressful situations. Although our algorithms are attempting to choose emotional support based on the general, “naive” competence of human speakers, we use as a running example situations that can arise when attending a medical emergency and awaiting expert help.
Original language | English |
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Pages (from-to) | 1-22 |
Number of pages | 22 |
Journal | International Journal of Human Computer Studies |
Volume | 97 |
DOIs | |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Bibliographical note
Acknowledgements This work was funded by the RCUK Digital Economy award to the dot.rural Digital Economy Hub, University of Aberdeen; award reference: EP/G066051/1. The dataset used by this paper can be acquired by emailing the first author. We thank Matt Dennis, Kirsten A. Smith and Michael Gibson for their contributions to the research.Keywords
- Adaptation
- Emotional support
- Stress