Despite the long history of studying instant messaging usage in organizations, we know very little about how todays people participate in group chat channels and interact with others. In this short note, we aim to update the existing knowledge on how group chat is used in the context of todays organizations. We have the privilege of collecting a total of 4300 publicly available group chat channels in Slack from an R&D department in a multinational IT company. Through qualitative coding of 100 channels, we identified 9 channel categories such as project based channels and event channels. We further defined a feature metric with 21 features to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition, we illustrated how these communication metrics could be used for analyzing teams collaboration activities. We focused on 117 project teams as we have their performance data, and further collected 54 out of the 117 teams Slack group data and generated the communication style metrics for each of them. With these data, we are able to build a regression model to reveal the relationship between these group communication styles and one indicator of the project team performance.
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