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

A Conceptual Framework

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalGroup Decision and Negotiation
DOIs
StateAccepted/In press - Dec 19 2016

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Keywords

  • Argumentation mining
  • Awareness support
  • Large-scale online open participative (LSOOP) activities
  • Rationale detection

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Arts and Humanities (miscellaneous)
  • Social Sciences(all)
  • Strategy and Management
  • Management of Technology and Innovation

Cite this

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title = "Toward the Automated Detection of Individuals’ Rationales in Large-Scale Online Open Participative Activities: A Conceptual Framework",
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.",
keywords = "Argumentation mining, Awareness support, Large-scale online open participative (LSOOP) activities, Rationale detection",
author = "Lu Xiao and Jennifer Stromer-Galley and {\'A}gnes S{\'a}ndor",
year = "2016",
month = "12",
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language = "English (US)",
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journal = "Group Decision and Negotiation",
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AU - Sándor, Ágnes

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KW - Rationale detection

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