"User Interfaces" and the Social Negotiation of Availability

TL;DR

This paper explores how availability in communication systems is negotiated socially rather than instantaneously determined, proposing machine learning methods to infer engagement and facilitate implicit negotiation.

Contribution

It introduces a novel approach to infer conversational engagement using machine learning to support social negotiation of availability.

Findings

Machine learning can infer engagement levels from conversational behavior.

Availability negotiation is often a social, joint process.

Proposed methods can facilitate implicit contact negotiation.

Abstract

In current presence or availability systems, the method of presenting a user's state often supposes an instantaneous notion of that state - for example, a visualization is rendered or an inference is made about the potential actions that might be consistent with a user's state. Drawing on observational research on the use of existing communication technology, we argue (as have others in the past) that determination of availability is often a joint process, and often one that takes the form of a negotiation (whether implicit or explicit). We briefly describe our current research on applying machine learning to infer degrees of conversational engagement from observed conversational behavior. Such inferences can be applied to facilitate the implicit negotiation of conversational engagement - in effect, helping users to weave together the act of contact with the act of determining availability.