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A recurrent stick breaking topic model for argument stance detection

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Abstract

Debate websites are valuable social media platforms for discussing controversial issues and gaining insights into diverse perspectives. However, with thousands of arguments on popular topics, browsing through entire discussions can be too time-consuming to extract a summary of the main viewpoints. To address this challenge, natural language processing techniques have been proposed to automatically identify argument structures and determine a user's stance on an issue. In addition to detecting stances, identifying topics that remain contentious between factions is especially important for policy-makers. However, the topics discussed by users with the same stance may vary in importance. To extract potential text topics, models based on probability and neural topic models have been proposed, with the latter being more effective due to improved computational cost and the ability to extract more consistent topics. This research proposes a two-stage stance detection model, which combines a neural topic model based on variational autoencoder and a recurrent neural network to learn the characteristics of an argument and enhance the model with subtopic features. The experimental results show that the model can obtain more coherent subtopics with a coherence of 0.42, and achieve an accuracy of nearly 70% in stance detection, demonstrating the effectiveness of the subtopic feature detection process.

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Data sharing does not apply to this article, as no datasets were generated or analyzed during the current study.

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Funding

The research is based on work supported by Taiwan Ministry of Science and Technology under Grant No. MOST 107–2410-H-006 040-MY3 and MOST 108–2511-H-0 06–009. We would like to thank partially research grant supported by "Higher Education SPROUT Project" and "Center for Innovative FinTech Business Models" of National Cheng Kung University (NCKU), sponsored by the Ministry of Education, Taiwan.

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Authors and Affiliations

  1. Institute of Information Management, National Cheng Kung University, Tainan, 701, Taiwan

    Hei -Chia Wang, Cendra Devayana Putra & Chia-Ying Wu

  2. Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan, 701, Taiwan

    Hei -Chia Wang

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  1. Hei -Chia Wang
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  2. Cendra Devayana Putra
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  3. Chia-Ying Wu
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Correspondence to Hei -Chia Wang.

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Wang, H.C., Putra, C.D. & Wu, CY. A recurrent stick breaking topic model for argument stance detection. Multimed Tools Appl 83, 38241–38266 (2024). https://doi.org/10.1007/s11042-023-16829-1

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  • DOI: https://doi.org/10.1007/s11042-023-16829-1

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