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The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse

Summary

Social science has long argued that political actors rely on metaphor and emotional appeals to persuade. Computational models of political text, however, had not taken either phenomenon into account. This paper asks whether explicitly teaching a model about metaphor and emotion improves its analysis of political language.

The authors build multi-task learning (MTL) models on RoBERTa-base. Each model pairs a main political task with one auxiliary task: word-level metaphor detection on the VU Amsterdam dataset, or sentence-level emotion classification on SemEval-2018 Task 1 tweets. The first eleven transformer layers are shared, while the top layer and the classifiers remain task-specific. Long news articles are split into subdocuments, which an attention layer combines.

Three document-level tasks are tested:

  • left/centre/right perspective of AllSides news;
  • Republican vs Democrat affiliation from politicians' Facebook posts;
  • framing dimensions in the Media Frames Corpus.

Joint training with metaphor significantly outperforms the single-task baseline on all three tasks. Joint training with emotion helps perspective and affiliation but leaves framing unchanged. The study offers computational evidence that these pragmatic devices carry measurable political signal.

Key points

  • Data:
    • perspective: 2,008 training and 5,761 test articles;
    • affiliation: posts from 412 US politicians, with no politician overlap across splits;
    • framing: 23,580 articles on five policy issues with 15 frame labels;
    • emotion: 10,097 English tweets.
  • Training: the main and auxiliary losses are mixed with a weight α. Some setups start with an auxiliary warm-up phase (α = 0.01). Results are averaged over 10 seeds and tested with an approximate permutation test.
  • Where gains come from:
    • perspective: mainly better recognition of right-leaning articles;
    • affiliation: mainly better identification of Democrats;
    • framing: largest gains for the security & defense, morality and fairness & equality frames.
  • Emotion analysis: anger, disgust and fear were predicted more often for Republican posts, while love and sadness appeared more for Democratic ones.
  • Code: released at github.com/LittlePea13/mtl_political_discourse.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Discourse analysis

Cited by

24 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 22 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.findings-emnlp.402

@inproceedings{cabot2020pragmatics,
  title = {{The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse}},
  author = {Cabot, Pere-Lluís Huguet and Dankers, Verna and Abadi, David and Fischer, Agneta and Shutova, Ekaterina},
  booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.findings-emnlp.402},
  url = {https://doi.org/10.18653/v1/2020.findings-emnlp.402}
}

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