DeepAI AI Chat
Log In Sign Up

Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics, and the Perception of Team Social Outcomes

by   Mingzhi Yu, et al.
University of Maryland
University of Pittsburgh

Multi-party linguistic entrainment refers to the phenomenon that speakers tend to speak more similarly during conversation. We first developed new measures of multi-party entrainment on features describing linguistic style, and then examined the relationship between entrainment and team characteristics in terms of gender composition, team size, and diversity. Next, we predicted the perception of team social outcomes using multi-party linguistic entrainment and team characteristics with a hierarchical regression model. We found that teams with greater gender diversity had higher minimum convergence than teams with less gender diversity. Entrainment contributed significantly to predicting perceived team social outcomes both alone and controlling for team characteristics.


page 1

page 2

page 3

page 4


What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings

Team composition is a central factor in determining the effectiveness of...

Synergistic Team Composition: A Computational Approach to Foster Diversity in Teams

Co-operative learning in heterogeneous teams refers to learning methods ...

Mimicry Is Presidential: Linguistic Style Matching in Presidential Debates and Improved Polling Numbers

The current research used the contexts of U.S. presidential debates and ...

Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports

Different linguistic expressions can conceptualize the same event from d...

The Wisdom of Polarized Crowds

As political polarization in the United States continues to rise, the qu...

Relating IS Developers' Attitudes to Engagement

Increasing effort is being directed to understanding the personality pro...

Predicting Cricket Outcomes using Bayesian Priors

This research has developed a statistical modeling procedure to predict ...