Interpersonal communication is essential to group creativity tasks, such as group brainstorming. However, what's easily overlooked in supporting group creativity is the potential use of nonverbal behavior in communication. When people communicate verbally (e.g., discussing a topic through conversation), they may also co-produce hand movements along with verbal contents as part of the communication process. Going beyond using external interventions for creativity gain, we examine the potential to facilitate worker's in-conversation gesture use as an intrinsic mechanism for bolstering group creativity. In this paper, we aim to gain a deeper understanding of the usage and function of hand gestures in computer-mediated group brainstorming. Through a laboratory study, we verified that metaphoric gestures, or producing spatial cues with hands to convey intended concepts, can best influence self's and partner's idea generation. Also, the positive effect of metaphoric gesture is independent of media richness of communication medium (e.g., whether there's visibility or not). The results help to fill in the knowledge gap regarding how gesture use may relate to group creativity, and reveal the potential viability of shaping metaphoric gesture use as an intrinsic creativity support.
Successful open source software (OSS) projects comprise freely observable, task-oriented social networks with hundreds or thousands of participants and large amounts of (textual and technical) discussion. The sheer volume of interactions and participants makes it challenging for participants to find relevant tasks, discussions and people. Tagging (
e.g
., @AmySmith) is a socio-technical practice that enables more focused discussion. By tagging important and relevant people, discussions can be advanced more effectively. However, for all but a few insiders, it can be difficult to identify important and/or relevant people. In this paper we study tagging in OSS projects from a socio-linguistics perspective. First we argue that
textual content per se
reveals a great deal about the status and identity of
who is speaking
and
who is being addressed
. Next, we suggest that this phenomenon can be usefully modeled using modern deep-learning methods. Finally, we illustrate the value of these approaches with tools that could assist people to find the important and relevant people for a discussion.
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