Context-aware conversation thread detection in multi-party chat
Ming Tan, Dakuo Wang, et al.
EMNLP-IJCNLP 2019
In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.
Ming Tan, Dakuo Wang, et al.
EMNLP-IJCNLP 2019
Michael Muller, Katja Neureiter, et al.
CSCW 2016
Pernille Bjørn, Casey Fiesler, et al.
GROUP 2018
Pavan K. Vatturi, Werner Geyer, et al.
CIKM 2008