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A Node Classification Approach for Dynamically Extracting the Structures of Online Discussions

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Advances in Artificial Intelligence (JSAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1357))

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Abstract

Online discussion platforms require extracting the discussion structure in order to support understanding the flow of these discussions. Towards this end, this paper proposes an approach that performs node classification which is the first step towards extracting the structure of online discussions. In this regard, the proposed approach employs a graph attention network (GAT) in order to directly learn the discussion structure. In specific, the GAT, which is a type of graph neural networks (GNNs), encodes the graph structures directly. In addition, the GAT, which is based on attention architecture, is able to deal with different graph structures. In order to evaluate the proposed approach, we have conducted a set of experiments on the persuasive essay dataset that is styled using the issue-based information system (IBIS). The experimental results show that the proposed approach is able to classify the nodes in online discussion structures accurately.

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Acknowledgements

This work was supported by JST CREST Grant Number JPMJCR15E1 including AIP challenge program, Japan.

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Authors and Affiliations

  1. Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya-shi, Aichi-ken, 466-8555, Japan

    Shota Suzuki, Ahmed Moustafa & Rafik Hadfi

  2. Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto-shi, Kyoto-fu, 606-8501, Japan

    Takayuki Ito

Authors
  1. Shota Suzuki
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  2. Takayuki Ito
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  3. Ahmed Moustafa
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  4. Rafik Hadfi
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Corresponding author

Correspondence to Shota Suzuki .

Editor information

Editors and Affiliations

  1. Kansai University, Suita, Osaka, Japan

    Katsutoshi Yada

  2. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  3. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  4. Department of Social Informatics, Kyoto University, Kyoto, Japan

    Takayuki Ito

  5. Division of Behavioral Science, Faculty of Letters, Chiba University, Chiba, Chiba, Japan

    Akinori Abe

  6. Department of Computer Science, Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Eri Sato-Shimokawara

  7. Mathematics and Informatics Center, The University of Tokyo, Tokyo, Japan

    Junichiro Mori

  8. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  9. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

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Suzuki, S., Ito, T., Moustafa, A., Hadfi, R. (2021). A Node Classification Approach for Dynamically Extracting the Structures of Online Discussions. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_1

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