Method measures how teamwork emerges from online collaboration
Teams without teams: a trace-based method for measuring team-like organization
Social and Information Networks
Summary
It can be hard to tell when people working together online are really acting like a team or just working separately. The authors created a way to measure the ‘teamness’ of a group based on the detailed activity logs from digital platforms, without needing to know in advance who is on a team. They tested this using millions of edits from Wikipedia articles about medicine, showing that teamwork can look different over time and for different tasks. This method helps identify subtle and changing forms of group work from the data alone.
What this means in practice
- •For online community managers: Measure the level and changes of teamwork in collaborative projects by analyzing activity logs without predefining teams.
- •For content platform designers: Identify patterns of teamlike organization in user contributions to improve tools supporting collaborative editing environments.
Tested on one dataset.
Authors
Thomas Baillie-Dawe, Cécile Bothorel, Nicolas Jullien
Abstract
Digital platforms provide increasingly detailed traces of collaborative activity, yet these traces do not directly reveal whether contributions remain largely independent or whether more structured forms of collective work emerge through interaction. This paper addresses the methodological problem of measuring such organization when the collective being studied is not defined as a team in advance. We propose a trace-based method for measuring teamness, understood as the degree to which collaborative activity expresses properties associated with teamwork. The method translates established dimensions of teamwork into behavioral indicators that can be recovered from digital traces. These indicators can be examined separately and summarized through a composite measure of their joint expression. It does not require predefined team membership and is designed to be adapted across collaborative settings. We illustrate the method using revision histories from 52,437 articles in the English Wikipedia's Medicine WikiProject, comprising more than 10 million human-authored edits. Using article-months as observation units, the application shows that different dimensions of teamlike organization display distinct patterns and that their joint expression can vary within the same articles over time. The contribution is a general measurement framework for examining graded and dynamic forms of collective organization directly from behavioral traces.