Defining and quantifying conversation quality in spontaneous interactions

Conference Paper (2020)
Author(s)

Navin Raj Prabhu (Student TU Delft)

C.A. Raman (TU Delft - Pattern Recognition and Bioinformatics)

Hayley Hung (TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2020 Navin Raj Prabhu, C.A. Raman, H.S. Hung
DOI related publication
https://doi.org/10.1145/3395035.3425966
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Navin Raj Prabhu, C.A. Raman, H.S. Hung
Research Group
Pattern Recognition and Bioinformatics
Pages (from-to)
196-205
ISBN (print)
9781450380027
Reuse Rights

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Abstract

Social interactions in general are multifaceted and there exists a wide set of factors and events that influence them. In this paper, we quantify social interactions with a holistic viewpoint on individual experiences, particularly focusing on non-task-directed spontaneous interactions. To achieve this, we design a novel perceived measure, the perceived Conversation Quality, which intends to quantify spontaneous interactions by accounting for several socio-dimensional aspects of individual experiences. To further quantitatively study spontaneous interactions, we devise a questionnaire which measures the perceived Conversation Quality, at both the individual- and at the group- level. Using the questionnaire, we collected perceived annotations for conversation quality in a publicly available dataset using naive annotators. The results of the analysis performed on the distribution and the inter-annotator agreeability shows that naive annotators tend to agree less in cases of low conversation quality samples, especially while annotating for group-level conversation quality.