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Journal article
Applying a Text-Based Affective Dialogue System in Psychological Research: Case Studies on the Effects of System Behaviour, Interaction Context and Social Exclusion
Cognitive computation, v 6(4), pp 872-891
01 Dec 2014
Abstract
This article presents two studies conducted with an affective dialogue system in which text-based system-user communication was used to model, generate and present different affective and social interaction scenarios. We specifically investigated the influence of interaction context and roles assigned to the system and the participants, as well as the impact of pre-structured social interaction patterns that were modelled to mimic aspects of "social exclusion" scenarios. The results of the first study demonstrate that both the social context of the interaction and the roles assigned to the system influence the system evaluation, interaction patterns, textual expressions of affective states, as well as emotional self-reports. The results observed for the second study show the system's ability to partially exclude a participant from a triadic conversation without triggering significantly different affective reactions or a more negative system evaluation. The experimental evidence provides insights on the perception, modelling and generation of affective and social cues in artificial systems that can be realized in different modalities, including the text modality, thus delivering valuable input for applying affective dialogue systems as tools for studying affect and social aspects in online communication.
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Details
- Title
- Applying a Text-Based Affective Dialogue System in Psychological Research: Case Studies on the Effects of System Behaviour, Interaction Context and Social Exclusion
- Creators
- Marcin Skowron - Austrian Research Institute for Artificial IntelligenceStefan Rank - Drexel UniversityAleksandra Swiderska - Jacobs UniversityDennis Kuester - Univ Bremen, D-28759 Bremen, GermanyArvid Kappas - Jacobs University
- Publication Details
- Cognitive computation, v 6(4), pp 872-891
- Publisher
- Springer Nature
- Number of pages
- 20
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Digital Media
- Web of Science ID
- WOS:000345994900020
- Scopus ID
- 2-s2.0-84916230463
- Other Identifier
- 991019167468604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Neurosciences