Name
Abstract | ||
|---|---|---|
We address the problem of automatically detecting participant's influence levels in meetings. The impact and social psychological background are discussed. The more influential a participant is, the more he or she influences the outcome of a meeting. Experiments on 40 meetings show that application of statistical (both dynamic and static) models while using simply obtainable features results in a best prediction performance of 70.59% when using a static model, a balanced training set, and three discrete classes: high, normal and low. Application of the detected levels are shown in various ways i.e. in a virtual meeting environment as well as in a meeting browser system. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1180995.1181047 | ICMI |
Keywords | Field | DocType |
influence level,influence ranking,social psychological background,discrete class,best prediction performance,balanced training set,obtainable features result,small group meeting,virtual meeting environment,static model,meeting browser system,various way,psychology,machine learning,static analysis,mathematical models | Training set,Static model,Computer science,Static analysis,Human–computer interaction,Artificial intelligence,Mathematical model,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-541-X | 49 | 3.29 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rutger Rienks | 1 | 168 | 13.14 |
| Dong Zhang | 2 | 646 | 38.04 |
| Daniel Gatica-Perez | 3 | 4182 | 276.74 |
| Wilfried Post | 4 | 49 | 3.29 |