Time is On My Side: Dynamics of Talk-Time Sharing in Video-chat Conversations

TL;DR

This paper introduces a computational framework to analyze and quantify talk-time sharing dynamics in video-chat conversations, revealing how different sharing patterns affect perception and offering tools for communication platform design.

Contribution

The work presents a novel typology and computational framework for analyzing talk-time sharing dynamics, with applications to improving video-chat platform design.

Findings

Balanced conversations are preferred over imbalanced ones.

Different sharing dynamics are perceived differently, even with similar overall balance.

The framework provides new tools for designing communication platforms.

Abstract

An intrinsic aspect of every conversation is the way talk-time is shared between multiple speakers. Conversations can be balanced, with each speaker claiming a similar amount of talk-time, or imbalanced when one talks disproportionately. Such overall distributions are the consequence of continuous negotiations between the speakers throughout the conversation: who should be talking at every point in time, and for how long? In this work we introduce a computational framework for quantifying both the conversation-level distribution of talk-time between speakers, as well as the lower-level dynamics that lead to it. We derive a typology of talk-time sharing dynamics structured by several intuitive axes of variation. By applying this framework to a large dataset of video-chats between strangers, we confirm that, perhaps unsurprisingly, different conversation-level distributions of talk-time are perceived differently by speakers, with balanced conversations being preferred over imbalanced ones, especially by those who end up talking less. Then we reveal that -- even when they lead to the same level of overall balance -- different types of talk-time sharing dynamics are perceived differently by the participants, highlighting the relevance of our newly introduced typology. Finally, we discuss how our framework offers new tools to designers of computer-mediated communication platforms, for both human-human and human-AI communication.