Context is Key: Mining Social Signals for Automatic Task Detection in Design Thinking Meetings

Despite the importance of team communication for successful collaborative problem solving, automated solutions for teams are notably absent from the literature. One promising avenue of research has been the development and integration of speech-based technology for team meetings. However, these technologies often fall short of meeting the needs of the teams as they do not take meeting context into consideration. In this paper, we demonstrate the efficacy of context detection with data collected during real team meetings. By capturing and analyzing social signals of rotation in team dynamics, we can demonstrate that different stages of collaborative problem solving using the design thinking methodology differ in their dynamics. Using supervised machine learning, we successfully predict design thinking mode with an overall F1 score of 0.68 and a best-performing sub-class model of 0.94. We believe this to be an essential step towards improving speech-based technology that aims to assist teams during meetings. Making these automated systems context-aware will enable them to provide teams with relevant information, such as resources or guidance.