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Determining political interests of issue-motivated groups on social media: joint topic models for issues, sentiment and stance

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  • Sandeepa Kannangara

    (University of New South Wales)

  • Wayne Wobcke

    (University of New South Wales
    University of New South Wales)

Abstract

Stance detection is an emerging research problem in opinion mining where the aim is to automatically determine from the text whether the author is for, against or neutral towards a proposition or target. In this paper, we propose a novel weakly supervised probabilistic topic model, Joint Issue-Sentiment-Stance Topic (JISST) model, for stance detection from political opinion in social media. The model automatically identifies the target issue and stance toward the target issue simultaneously from the text. Unlike other machine learning approaches to stance classification which require labelled data for training classifiers, JISST requires only a small number of seed words for each issue and stance and a sentiment lexicon. The model is evaluated on two datasets in the political domain: a Facebook dataset which contains posts of politically motivated Facebook groups in Australia and a Twitter dataset which was published for the SemEval 2016 competition. Experimental results demonstrate that JISST outperforms both weakly supervised and supervised baselines for stance and issue classification.

Suggested Citation

  • Sandeepa Kannangara & Wayne Wobcke, 2022. "Determining political interests of issue-motivated groups on social media: joint topic models for issues, sentiment and stance," Journal of Computational Social Science, Springer, vol. 5(1), pages 811-840, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00146-4
    DOI: 10.1007/s42001-021-00146-4
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    References listed on IDEAS

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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
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