  Having a group discussion with the members holding conflicting viewpoints is
difficult. It is especially challenging for machine-mediated discussions in
which the subtle social cues are hard to notice. We present a fully automated
videochat framework that can automatically analyze audio-video data of the
participants and provide real-time feedback on participation, interruption,
volume, and facial emotion. In a heated discourse, these features are
especially aligned with the undesired characteristics of dominating the
conversation without taking turns, interrupting constantly, raising voice, and
expressing negative emotion. We conduct a treatment-control user study with 40
participants having 20 sessions in total. We analyze the immediate and the
reflective effects of real-time feedback on participants. Our findings show
that while real-time feedback can make the ongoing discussion significantly
less spontaneous, its effects propagate to successive sessions bringing
significantly more expressiveness to the team. Our explorations with instant
and propagated impacts of real-time feedback can be useful for developing
design strategies for various collaborative environments.
