Name
Abstract | ||
|---|---|---|
Language both influences and indicates group behavior, and we need tools that let us study the content of what is communicated. While one could annotate these spoken dialogue acts by hand, this is a tedious, not scalable process. We present Meeter, a tool for automatically detecting information sharing, shared understanding, word counts, and group activation in spoken interactions. The contribution of our work is two-fold: (1) We validated the tool by showing that the measures computed by Meeter align with human-generated labels, and (2) we demonstrated the value of Meeter as a research tool by quantifying aspects of group behavior using those measures and deriving novel findings from that. Our tool is valuable for researchers conducting group science, as well as those designing groupware systems.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3359132 | Proceedings of the ACM on Human-Computer Interaction |
Keywords | Field | DocType |
brainstorming, groups, machine learning, natural language processing, speech processing | Brainstorming,Computer science,Human–computer interaction | Journal |
Volume | Issue | Citations |
3 | CSCW | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernd Huber | 1 | 26 | 3.20 |
| Stuart M. Shieber | 2 | 2124 | 368.36 |
| Krzysztof Z. Gajos | 3 | 1837 | 127.94 |