Abstract
In computer-mediated communication (CMC), the lack of non-verbal visual cues in text communication can cause misunderstanding, as there may not be enough social cues (e.g., facial expression) for one to determine how their partners feel and whether they understand the messages. However, video-mediated communication with rich social cues including how one looks like may raise privacy concerns and social biases. Participants of group conversation also face the difficulty in managing rich visual cues produced by multiple group members. In this thesis, we propose an interaction technique namely KinChat that combines text chat with feature-based visualization of facial expression that uses motion sensor and 2D graphics to trace and represent interlocutors’ faces during conversations. We conducted two exploratory studies to examine the utilities of this feature-based visualization on de-identification and awareness of facial expression, as well as the broader impact on the outcomes of interactive communication. The results indicate that feature-based visualization of facial expression can preserve both awareness of facial expression and privacy, and can lead to improved understanding and reduced communication anxiety when applied to communication settings. In addition, we designed an extra application, which represents general emotion and social atmosphere of a group with a single synthesized visualization of multiple users’ faces. We also conducted a pilot study to examine this application design.