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Toward the Automated Detection of Individuals’ Rationales in Large-Scale Online Open Participative Activities: A Conceptual Framework

Author

Listed:
  • Lu Xiao

    (Syracuse University)

  • Jennifer Stromer-Galley

    (Syracuse University)

  • Ágnes Sándor

    (Xerox Research Centre Europe)

Abstract

In large-scale online open participative (LSOOP) activities, participants can join and leave at any time, and they often do not have a history of working together. Although the communication history is usually accessible to the participants in the environment, it is time consuming for them to process the communication data because of the large volume of messages. These characteristics make it difficult for one to keep track of, identify, and interpret the others’ ideas, opinions, and their rationales in LSOOP activities. We argue for a computational approach that automatically identifies and extracts the rationales from LSOOP communication data and presents them to the participants through rationale-based awareness tools. In this paper we bring together different and hitherto independent lines of research, and propose to use them in a conceptual framework integrating three analytical aspects related to the detection of rationales: linguistic, informational, and argumentative and communicative. We also review the design effort on offering rationale-based awareness in the LSOOP activities.

Suggested Citation

  • Lu Xiao & Jennifer Stromer-Galley & Ágnes Sándor, 2017. "Toward the Automated Detection of Individuals’ Rationales in Large-Scale Online Open Participative Activities: A Conceptual Framework," Group Decision and Negotiation, Springer, vol. 26(5), pages 891-910, September.
    • Handle: RePEc:spr:grdene:v:26:y:2017:i:5:d:10.1007_s10726-016-9516-4
    DOI: 10.1007/s10726-016-9516-4
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    References listed on IDEAS

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    1. Lu Xiao, 2014. "Effects of rationale awareness in online ideation crowdsourcing tasks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(8), pages 1707-1720, August.
    2. Andreas Peldszus & Manfred Stede, 2013. "From Argument Diagrams to Argumentation Mining in Texts: A Survey," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 7(1), pages 1-31, January.
    3. Lu Xiao & Nicole Askin, 2014. "What influences online deliberation? A wikipedia study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 898-910, May.
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