# Defining and Quantifying Conversation Quality in Spontaneous Interactions

arXiv: https://arxiv.org/abs/2009.12842 · HTML: https://mlsanity.com/paper/2009.12842/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild](https://mlsanity.com/paper/2205.05177/) (2022)
- [End-To-End Label Uncertainty Modeling for Speech-based Arousal Recognition Using Bayesian Neural Networks](https://mlsanity.com/paper/2110.03299/) (2021)
- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning](https://mlsanity.com/paper/2209.15449/) (2022)
- [Label Uncertainty Modeling and Prediction for Speech Emotion Recognition using t-Distributions](https://mlsanity.com/paper/2207.12135/) (2022)
- [Conversation Group Detection With Spatio-Temporal Context](https://mlsanity.com/paper/2206.02559/) (2022)
- [Towards Multimodal Social Conversations with Robots: Using Vision-Language Models](https://mlsanity.com/paper/2507.19196/) (2025)
- [REWIND Dataset: Privacy-preserving Speaking Status Segmentation from Multimodal Body Movement Signals in the Wild](https://mlsanity.com/paper/2403.01229/) (2024)

## Abstract (arXiv)

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.