team characteristic
Yu
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.
Investigating the Relationship between Multi-Party Linguistic Entrainment, Team Characteristics and the Perception of Team Social Outcomes
Yu, Mingzhi (University of Pittsburgh) | Litman, Diane (University of Pittsburgh) | Paletz, Susannah (University of Maryland)
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.
- North America > United States > Texas (0.04)
- North America > United States > New York (0.04)
- North America > United States > Maryland (0.04)
To beat Vegas bookies at the World Cup, these statisticians turned to artificial intelligence
When it comes to sports betting, most people lose. But during the 2014 World Cup, a team of statisticians beat the bookmakers. They correctly predicted Germany -- their home country and 6-to-1 underdogs -- as the final champions and raked in a 30 percent return with bets placed on regular matches. This year, the team, led by Andreas Groll of Germany's Technical University of Dortmund, is back with an artificial intelligence program with even better odds. As of Friday morning, it had correctly predicted 15 of 24 games -- the winners, the losers and those who tied. As for the ultimate victor, their new model has picked Spain, but only if Germany falters before the final.
- Europe > Spain (0.27)
- North America > Mexico (0.06)
- Europe > Serbia (0.05)
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