Argument detection in online discussion

A theory based approach

Guangxuan Zhang, Sandeep Purao, Yilu Zhou, Heng Xu

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

Abstract

The rapid growth of online communication has dramatically changed the manner in which arguments take place. Participants in virtual teams and communities continue to face challenges to detect and make sense of arguments when the constituent elements of an argument are scattered in prolonged online discussions. Few methods or tools are available for the detection of arguments from available sources. This paper develops a theory-based argument detection model. Drawing on the argumentation theory, we propose a model for argument detection. It is composed of features that reflect five categories of argumentation functions, including announcement, reasoning, modality, transition, and affect, and another language features that are informative for recognizing argument. The evaluation results show that the model achieves higher accuracy and recall in detecting arguments in message sets, compared to baseline models. The paper also presents an illustrative example to show application of the model in practice.

Original languageEnglish (US)
Title of host publicationAMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems
PublisherAssociation for Information Systems
StatePublished - 2016
Event22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016 - San Diego, United States
Duration: Aug 11 2016Aug 14 2016

Other

Other22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016
CountryUnited States
CitySan Diego
Period8/11/168/14/16

Fingerprint

Communication

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Information Systems

Cite this

Zhang, G., Purao, S., Zhou, Y., & Xu, H. (2016). Argument detection in online discussion: A theory based approach. In AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems Association for Information Systems.
Zhang, Guangxuan ; Purao, Sandeep ; Zhou, Yilu ; Xu, Heng. / Argument detection in online discussion : A theory based approach. AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems. Association for Information Systems, 2016.
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title = "Argument detection in online discussion: A theory based approach",
abstract = "The rapid growth of online communication has dramatically changed the manner in which arguments take place. Participants in virtual teams and communities continue to face challenges to detect and make sense of arguments when the constituent elements of an argument are scattered in prolonged online discussions. Few methods or tools are available for the detection of arguments from available sources. This paper develops a theory-based argument detection model. Drawing on the argumentation theory, we propose a model for argument detection. It is composed of features that reflect five categories of argumentation functions, including announcement, reasoning, modality, transition, and affect, and another language features that are informative for recognizing argument. The evaluation results show that the model achieves higher accuracy and recall in detecting arguments in message sets, compared to baseline models. The paper also presents an illustrative example to show application of the model in practice.",
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Zhang, G, Purao, S, Zhou, Y & Xu, H 2016, Argument detection in online discussion: A theory based approach. in AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems. Association for Information Systems, 22nd Americas Conference on Information Systems: Surfing the IT Innovation Wave, AMCIS 2016, San Diego, United States, 8/11/16.

Argument detection in online discussion : A theory based approach. / Zhang, Guangxuan; Purao, Sandeep; Zhou, Yilu; Xu, Heng.

AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems. Association for Information Systems, 2016.

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

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AU - Xu, Heng

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N2 - The rapid growth of online communication has dramatically changed the manner in which arguments take place. Participants in virtual teams and communities continue to face challenges to detect and make sense of arguments when the constituent elements of an argument are scattered in prolonged online discussions. Few methods or tools are available for the detection of arguments from available sources. This paper develops a theory-based argument detection model. Drawing on the argumentation theory, we propose a model for argument detection. It is composed of features that reflect five categories of argumentation functions, including announcement, reasoning, modality, transition, and affect, and another language features that are informative for recognizing argument. The evaluation results show that the model achieves higher accuracy and recall in detecting arguments in message sets, compared to baseline models. The paper also presents an illustrative example to show application of the model in practice.

AB - The rapid growth of online communication has dramatically changed the manner in which arguments take place. Participants in virtual teams and communities continue to face challenges to detect and make sense of arguments when the constituent elements of an argument are scattered in prolonged online discussions. Few methods or tools are available for the detection of arguments from available sources. This paper develops a theory-based argument detection model. Drawing on the argumentation theory, we propose a model for argument detection. It is composed of features that reflect five categories of argumentation functions, including announcement, reasoning, modality, transition, and affect, and another language features that are informative for recognizing argument. The evaluation results show that the model achieves higher accuracy and recall in detecting arguments in message sets, compared to baseline models. The paper also presents an illustrative example to show application of the model in practice.

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Zhang G, Purao S, Zhou Y, Xu H. Argument detection in online discussion: A theory based approach. In AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems. Association for Information Systems. 2016