Learning About Meetings
This work addresses the problem of understanding complex meeting dynamics for researchers and practitioners, though it appears incremental as it builds on existing meeting analysis methods.
The paper tackled the challenge of studying meetings by using a data-driven approach to analyze social signals and interpersonal dynamics, providing tentative evidence for automatically detecting key decision moments, identifying common patterns in social dialogue acts, predicting remaining meeting time during decisions, and predicting proposal acceptance based on persuasive language.
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.