Geographically distributed collaborative teams often rely on synchronous text-based online communication for accomplishing tasks and maintaining social contact. This technology leaves a trace that can help researchers understand the expression and dynamics of emotion or affect in distributed groups. Although manual labeling of emotion in chat logs has shed light on complex group communication phenomena, scaling this process to larger data sets through automation is difficult, especially given the informal nature of chat communication. In this work we describe an open-source pipeline of automated classifiers of affect in chat logs called ALOE. Interpreting affect as a dynamic, contextualized process, we explain our development and application of this method to four years of chat logs from a longitudinal study of a multi-cultural distributed scientific collaboration. With ground truth generated through manual labeling of affect over a subset of the chat logs, our approach can successfully identify many commonly occurring types of affect.
Cecilia Aragon is an associate professor in the Department of Human Centered Design & Engineering at the University of Washington, where she directs the Scientific Collaboration and Creativity Lab. She holds a faculty position with the eScience Institute and adjunct positions with Computer Science and Engineering, Electrical Engineering, and the iSchool. Before arriving at UW in 2010, she was a computer scientist in the Computational Research Division at Lawrence Berkeley National Laboratory for six years, after earning her Ph.D. in computer science from UC Berkeley in 2004. She received her B.S. in mathematics from the California Institute of Technology.
Her current research focuses on human-computer interaction (HCI) and computer-supported cooperative work (CSCW), emotion in informal text communication, visual analytics, collaborative games, and how social media and new methods of computer-mediated communication are changing scientific practice.
She has authored or co-authored over 160 publications in HCI, visual analytics, machine learning, and astrophysics. Her research has been recognized with six Best Paper awards since 2004. She won the Distinguished Alumni Award in Computer Science from UC Berkeley in 2013, the Faculty Innovator in Teaching Award from her department at UW that same year, and was named one of the Top 25 Women of 2009 by Hispanic Business Magazine. In 2008, she received the Presidential Early Career Award for Scientists and Engineers (PECASE) for her work in data-intensive science.