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Examining Political Rhetoric with Epistemic Stance Detection

Paper · 2022 · Ankita Gupta, Su Lin Blodgett, Justin H Gross et al.

Summary

Political writers constantly signal how firmly they stand behind a claim. They hedge, attribute views to others, deny opponents' assertions, or present their own opinions as someone else's. Political scientists have mostly tracked these moves by hand. This paper automates the analysis by adapting epistemic stance detection from computational semantics. For each clause-level event, the task asks whether a given source (the author or an entity mentioned in the text) treats it as certainly true, probably true, certainly false, uncommitted, or expresses no epistemic view at all. The authors fine-tune a straightforward RoBERTa model on the multi-source annotations of FactBank. It matches or beats a more elaborate state-of-the-art system (Qian et al., 2018) while staying easy for social scientists to use. They then apply it to a collection of U.S. political books, the Mass Market Manifestos corpus, to identify the belief holders that left- and right-leaning authors cite most often. The resulting lists are politically meaningful, separating trusted allies from opponents used as bogeymen. This shows that semantic stance modeling can support large-scale study of rhetoric.

Key points

  • Training data is FactBank (Saurí and Pustejovsky), expert-annotated English news with discrete stance labels for several sources per event. Example labels are CT+, CT-, PR+, Uu and NE (non-epistemic).
  • The model is a fine-tuned RoBERTa for multi-source stance prediction, performing on par with the more complex model of Qian et al. (2018). Code and resources are released as ExPRES (github.com/slanglab/ExPRES).
  • The application corpus is Mass-Market Manifestos (MMM): 370 contemporary U.S. political opinion books by ideologically diverse authors. Belief-holder extraction is compared against plain named entity recognition.
  • For belief holders appearing in ≥ 8 books, a left-right citation ratio is computed using a book pseudocount of 1. A political scientist then labeled the top-20 entries per side as respected or opposed.
  • The analysis repeated for holders in ≥ 100 books shows familiar patterns:
    • The left leans on technocratic authority (economists, scientists, research).
    • The right respects the founders, whose ratio of 0.308 is the most skewed, and treats the media with derision.
  • An appendix replicates a manual case study of political leaders' commitment levels (Jalilifar and Alavi, 2011). It also examines model behavior on negative polarity items and different holder types.

Interesting ideas

  • I1. Annotate every (source, event) pair as a full grid, so a sentence with 4 sources and 3 events yields 12 stance labels; this separates the author's own commitment from the commitments the author reports for others.
  • I2. Include an explicit non-epistemic label for sources that are mentioned but whose beliefs about an event are not expressed, instead of forcing a positive/negative guess.
  • I3. Identify belief holders through stance (whose views the author conveys) rather than through NER, which finds mentions but not epistemic roles.
  • I4. Rank items by a smoothed ratio of document proportions (pseudocount of 1) with a minimum-frequency threshold, then have an expert label only the top of the list.
  • I5. Validate an automatic tool by reproducing a known manual study and by probing specific constructions such as negative polarity items.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): Each speech-act or reporting frame could carry a per-source stance slot, so "X said Y" records X's commitment to Y while the narrator's commitment stays uncommitted.
  • I2 (world): The world model could store "belief unknown" as a distinct state per entity, so the answer stage does not infer that a character believes something just because it appears near them.
  • I3 (frames): Reporting verbs and their UD subjects could be promoted to belief-holder entities, giving coreference a richer target than named spans.
  • I4 (learn): The same smoothed contrast could surface lexicon or frame items skewed toward one genre or witness parser, cheaply flagging candidates for human or LLM-teacher review.
  • I5 (learn): Evaluation suites could include replications of published hand analyses and targeted negation/NPI probes as controls, checking that stance-related frames flip correctly.

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

 Institutions

🏢MicrosoftRedmond · United States240 works · 123,537 citations🎓University of Massachusetts AmherstAmherst Center · United States67 works · 64,591 citations

📚 Shelves 7 Computer Science · 9,086 works Artificial Intelligence · 8,889 works Arts and Humanities · 9,296 works Language and Linguistics · 9,206 works Social Sciences · 744 works General Social Sciences · 87 works Literature and Literary Theory · 340 works

🧭 Topics 1 Sentiment & emotion · 608 works

Cite

Cite as Ankita Gupta, Su Lin Blodgett, Justin H Gross and Brendan O'Connor. 2022. Examining Political Rhetoric with Epistemic Stance Detection. Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.nlpcss-1.11 · arXiv 2212.14486

@inproceedings{gupta2022examining,
  title = {{Examining Political Rhetoric with Epistemic Stance Detection}},
  author = {Ankita Gupta, Su Lin Blodgett, Justin H Gross and Brendan O'Connor},
  booktitle = {Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)},
  year = {2022},
  doi = {10.18653/v1/2022.nlpcss-1.11},
  eprint = {2212.14486},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.nlpcss-1.11}
}

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