Learning About Meetings

TL;DR

This paper explores a data-driven approach to understanding meetings, demonstrating that key decisions and outcomes can be predicted from dialogue analysis and social signals.

Contribution

It introduces methods to automatically detect decision points, identify social dialogue patterns, and predict meeting outcomes based on language and timing.

Findings

Detection of key decision points from dialogue acts

Patterns in social dialogue acts during meetings

Prediction of decision acceptance based on language

Abstract

Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. Our aim this work is to use a data-driven approach to the science of meetings. We provide tentative evidence that: i) it is possible to automatically detect when during the meeting a key decision is taking place, from analyzing only the local dialogue acts, ii) there are common patterns in the way social dialogue acts are interspersed throughout a meeting, iii) at the time key decisions are made, the amount of time left in the meeting can be predicted from the amount of time that has passed, iv) it is often possible to predict whether a proposal during a meeting will be accepted or rejected based entirely on the language (the set of persuasive words) used by the speaker.