Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work

Guangxuan Zhang, Yilu Zhou, Sandeep Purao, Heng Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAdvances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings
EditorsZhanhuai Li, Tok Wang Ling, Guoliang Li, Jiaheng Lu, Carson Woo, Mong Li Lee
PublisherSpringer Verlag
Pages295-305
Number of pages11
ISBN (Print)9783030013905
DOIs
StatePublished - Jan 1 2018
Event37th International Conference on Conceptual Modeling, ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME - Xi'an, China
Duration: Oct 22 2018Oct 25 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11158 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other37th International Conference on Conceptual Modeling, ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME
CountryChina
CityXi'an
Period10/22/1810/25/18

Fingerprint

Collaborative Work
Trace
Interaction
Model
Argumentation
Conceptual Model
Modality
Conflict
Reasoning
Evaluation

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhang, G., Zhou, Y., Purao, S., & Xu, H. (2018). Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. In Z. Li, T. W. Ling, G. Li, J. Lu, C. Woo, & M. L. Lee (Eds.), Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings (pp. 295-305). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11158 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01391-2_34
Zhang, Guangxuan ; Zhou, Yilu ; Purao, Sandeep ; Xu, Heng. / Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings. editor / Zhanhuai Li ; Tok Wang Ling ; Guoliang Li ; Jiaheng Lu ; Carson Woo ; Mong Li Lee. Springer Verlag, 2018. pp. 295-305 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Zhang, G, Zhou, Y, Purao, S & Xu, H 2018, Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. in Z Li, TW Ling, G Li, J Lu, C Woo & ML Lee (eds), Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11158 LNCS, Springer Verlag, pp. 295-305, 37th International Conference on Conceptual Modeling, ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, Xi'an, China, 10/22/18. https://doi.org/10.1007/978-3-030-01391-2_34

Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. / Zhang, Guangxuan; Zhou, Yilu; Purao, Sandeep; Xu, Heng.

Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings. ed. / Zhanhuai Li; Tok Wang Ling; Guoliang Li; Jiaheng Lu; Carson Woo; Mong Li Lee. Springer Verlag, 2018. p. 295-305 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11158 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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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Zhang G, Zhou Y, Purao S, Xu H. Extracting Conflict Models from Interaction Traces in Virtual Collaborative Work. In Li Z, Ling TW, Li G, Lu J, Woo C, Lee ML, editors, Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings. Springer Verlag. 2018. p. 295-305. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-01391-2_34