TY - JOUR
T1 - Toward the Automated Detection of Individuals’ Rationales in Large-Scale Online Open Participative Activities
T2 - A Conceptual Framework
AU - Xiao, Lu
AU - Stromer-Galley, Jennifer
AU - Sándor, Ágnes
N1 - Funding Information:
This research is mainly supported by the Discovery Grant of Natural Sciences and Engineering Research Council of Canada (NSERC). The first author also thanks Xerox Research Center in Europe (XRCE) and Syracuse University for hosting her research leave during which the main ideas of this paper evolve.
Publisher Copyright:
© 2016, Springer Science+Business Media Dordrecht.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - 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.
AB - 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.
KW - Argumentation mining
KW - Awareness support
KW - Large-scale online open participative (LSOOP) activities
KW - Rationale detection
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U2 - 10.1007/s10726-016-9516-4
DO - 10.1007/s10726-016-9516-4
M3 - Article
AN - SCOPUS:85006365123
SN - 0926-2644
VL - 26
SP - 891
EP - 910
JO - Group Decision and Negotiation
JF - Group Decision and Negotiation
IS - 5
ER -