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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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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.

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Notes

  1. http://alt.qcri.org/semeval2016/.

  2. We have experimented with symmetric and asymmetric settings for each hyperparameter. Experimental results show that symmetric hyperparameters yield the best results.

  3. Although Task 6B of the SemEval 2016 was a weakly supervised task, we did not use the Task 6B dataset since there is only one issue/target for the entire dataset.

  4. https://australia.isidewith.com/polls, https://theconversation.com/2016-the-year-that-was-politics-and-society-70186.

  5. http://jgibblda.sourceforge.net/.

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Acknowledgements

This work was supported by Data to Decisions Cooperative Research Centre. We would like to thank Ming En Chin for annotating the dataset. The first author would also like to thank Michael Bain and Alfred Krzywicki for their continuous mentoring and constant support.

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  1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, 2052, Australia

    Sandeepa Kannangara & Wayne Wobcke

  2. UNSW Data Science Hub (uDASH), University of New South Wales, Sydney, NSW, 2052, Australia

    Wayne Wobcke

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Kannangara, S., Wobcke, W. Determining political interests of issue-motivated groups on social media: joint topic models for issues, sentiment and stance. J Comput Soc Sc 5, 811–840 (2022). https://doi.org/10.1007/s42001-021-00146-4

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