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ML-SPEAK: A theory-guided machine learning method for studying and predicting conversational turn-taking patterns.
Lisa R O'Bryan1, Madeline Navarro1, Juan Segundo Hevia2
1Department of Electrical and Computer Engineering, Rice University.
This study introduces ML-SPEAK, a computational model predicting team communication dynamics from personality traits. It accurately forecasts speaking patterns, offering insights for team staffing and training.
Area of Science:
- Psychological Sciences
- Computational Social Science
- Team Dynamics
Background:
- Predicting team dynamics from personality traits is a persistent challenge.
- Existing models like input-process-output lack dynamic capabilities for complex team interactions.
- Understanding team composition's impact on processes is crucial for research and practical applications.
Purpose of the Study:
- To develop a computational model for analyzing conversational turn-taking in self-organized teams.
- To investigate the relationship between individual personality traits and team communication dynamics.
- To predict group communication patterns based solely on team trait composition.
Main Methods:
- Developed the ML-SPEAK computational model focusing on conversational turn-taking patterns.
- Trained the model on conversational data from teams with known trait compositions.
- Evaluated model performance using simulated data and real-world data from student teams.
Main Results:
- The ML-SPEAK model accurately predicts speaking turn sequences, outperforming baseline models.
- The model reveals novel relationships between team members' personality traits and their communication patterns.
- Demonstrated the model's ability to predict group communication based on team trait composition.
Conclusions:
- The ML-SPEAK model provides a data-driven, dynamic approach to understanding team processes.
- It bridges the gap between individual characteristics and emergent team communication patterns.
- Offers potential for informing team process theories and optimizing team staffing and training.

