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Stanceosaurus: Classifying Stance Towards Multicultural Misinformation

Paper · 2022 · Jonathan Zheng, Ashutosh Baheti, Tarek Naous et al.

Summary

Stanceosaurus is a Twitter corpus for detecting how posts relate to false claims. Earlier misinformation stance datasets mostly covered Western events or a handful of topics. This one contains 28,033 tweets in English, Hindi and Arabic. Each tweet is manually labeled for its stance toward one of 251 claims drawn from 15 fact-checking outlets in India, Singapore, Australia, New Zealand, Canada, the US, Europe and the Arab world.

The labels are finer than usual: five main classes (Irrelevant, Supporting, Refuting, Discussing, Querying), plus subcategories that separate explicit from implicit stance. Real-world stance is heavily skewed, with support and refutation both rare. To handle this, the authors fine-tune BERTweet large with class-balanced focal loss instead of cross-entropy.

The resulting models generalize to unseen claims and to claims from regions absent in training. Multilingual models transfer zero-shot to Hindi and Arabic. An EasyAdapt-style domain adaptation also lets RumourEval-2019 data improve results. The resource matters because it enables research on misinformation outside Western contexts and across languages.

Key points

  • 28,033 tweets, 251 claims and 15 sources (nine English, three Hindi, three Arabic). Of the claims, 144 are regional and the rest international, with dates spanning 2012 to 2022.
  • Claims are complete sentences rather than broad topics or entities, which lets the corpus cover many more subjects than SemEval-2016, WTWT or RumourEval.
  • BERTweet large with class-balanced focal loss reaches 66.8 F1 on 3-way and 61.0 F1 on 5-way English classification. Focal loss consistently beats cross-entropy.
  • Zero-shot cross-lingual transfer reaches roughly 50–53 F1 on Hindi and Arabic. The abstract and introduction disagree on which language gets 53.1 and which gets 50.4.
  • In the "unseen countries" setting, models are trained on international claims and tested on regional ones. Aggregate F1 is 57.1, a bit below the unseen-claims split. EasyAdapt yields one model that is best on Stanceosaurus and roughly matches in-domain RumourEval performance.

Interesting ideas

  • I1. Stance labels get extra subcategories marking whether the attitude is stated outright or only implied, so the hard implicit cases can be measured separately.
  • I2. Class-balanced focal loss reweights training toward rare and hard examples, using an "effective number of samples" per class rather than raw counts. This helps whenever the natural label distribution is very skewed.
  • I3. Generalization is tested by holding out an entire slice of the world: train on international claims, test on region-specific ones. This exposes reliance on familiar content rather than on the task itself.
  • I4. EasyAdapt merges two datasets built under different collection conventions by keeping shared parameters plus domain-specific ones. A single model then benefits from both without one dataset's conventions overriding the other's.
  • I5. The authors note that retrieving candidate examples by semantic similarity to the claim (as CovidLies did with BERTScore) skews data toward agreement and away from rebuttals, so they used curated keyword queries instead.

For Mova

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

  • I1 (frames): prag could tag each speech-act or attitude frame as explicit vs. implied, and report implicature/irony accuracy separately from literal assertions.
  • I2 (learn): rare semantic roles or rare dependency relations in silver data could be weighted this way so the trainer stops collapsing onto frequent labels.
  • I3 (learn): evaluation could hold out whole genres or topic domains (e.g., train on school texts, test on fables) as a structural control alongside negative controls.
  • I4 (tree): gparse could train on several treebanks with differing UD annotation habits, marking the source dialect, instead of forcing all of them through lossy conversion first.
  • I5 (learn): when selecting sentences for silver data or LLM-teacher labeling, avoid picking only examples similar to existing ones, or the training pool will under-represent contrary patterns.

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

🎓Georgia Institute of TechnologyAtlanta · United States133 works · 24,497 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Semitic languages · 2,908 works

Cite

Cite as Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu and Alan Ritter. 2022. Stanceosaurus: Classifying Stance Towards Multicultural Misinformation. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.emnlp-main.138

@inproceedings{zheng2022stanceosaurus,
  title = {{Stanceosaurus: Classifying Stance Towards Multicultural Misinformation}},
  author = {Jonathan Zheng, Ashutosh Baheti, Tarek Naous, Wei Xu and Alan Ritter},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.138},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.138}
}

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