Abstract
This paper develops a model of conflicts that relies on extracting text and argument features from traces of interactions in collaborative work. Much prior research about collaborative work is aimed at improving the support for virtual work. In contrast, we are interested in detecting conflicts in collaborative work because conflict undetected can escalate and cause disruptions to productive work. It is a difficult problem because it requires untangling conflict-related interactions from normal interactions. Few models or methods are available for this purpose. The extracted features, interpreted with the help of foundational theories, suggests a conceptual model of conflicts that include categories of argumentation such as reasoning and modality; and informative language features. We illustrate the extraction approach and the model with a dataset from Bugzilla. The paper concludes with a discussion of evaluation possibilities and potential implications of the approach for detecting and managing conflicts in collaborative work.
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Acknowledgements
The work reported has been funded by the National Science Foundation under award number CNS 1551004. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF). We also acknowledge the commentary from the review team that has helped us refine the paper.
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Zhang, G., Zhou, Y., Purao, S., Xu, H. (2018). Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. In: Woo, C., Lu, J., Li, Z., Ling, T., Li, G., Lee, M. (eds) Advances in Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11158. Springer, Cham. https://doi.org/10.1007/978-3-030-01391-2_34
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