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Stanceosaurus: Classifying Stance Towards Multilingual Misinformation

Summary

Stance detection toward false claims can help moderators track misinformation, but existing Twitter corpora cover few topics and are mostly Western. The authors built Stanceosaurus: 28,033 tweets in English, Hindi and Arabic, each labeled for stance toward one of 251 claims. The claims come from 15 fact-checking outlets covering India, Singapore, Australia, New Zealand, Canada, the US, Europe and the Arab World. Tweets are labeled with five classes (Irrelevant, Supporting, Refuting, Discussing, Querying), and subcategories separate explicit from implicit stance. They fine-tuned BERTweet large with class-balanced focal loss to cope with heavy label skew. The model holds up on unseen claims and on regional claims from countries absent in training, and it transfers zero-shot to Hindi and Arabic. A variant of EasyAdapt lets them add RumourEval-2019 data, which improves results on Stanceosaurus. At release this was the largest stance-toward-claims dataset. It matters because it enables multilingual and cross-cultural work on misinformation.

Key points

  • Corpus size and coverage: 28,033 tweets and 251 claims, of which 144 were judged regional by manual inspection.
    • Sources: nine English, three Hindi and three Arabic fact-checking sites.
    • Claims were posted between 2012 and early 2022.
  • Label distribution is strongly skewed:
    • Discussing: 11,665
    • Irrelevant: 9,998
    • Supporting: 4,270
    • Refuting: 1,655
    • Querying: 445
  • English results: BERTweet large with class-balanced focal loss reaches 66.8 macro F1 on 3-way stance and 61.0 on 5-way stance. Focal loss consistently beats cross-entropy.
  • Cross-lingual results: zero-shot 5-way classification yields about 50–53 F1 on Hindi and Arabic. The paper reports 53.1 and 50.4, though it assigns these numbers to the two languages inconsistently between abstract and introduction.
  • Transfer settings:
    • Training on international claims and testing on regional ones gives 57.1 aggregate F1, lower than the unseen-claims setting.
    • Plain cross-dataset training with RumourEval drops performance sharply.
    • EasyAdapt yields one model that is best on Stanceosaurus and on par with in-domain RumourEval.

Interesting ideas

  • I1. Tie stance to a specific full-sentence claim rather than a broad topic; this makes the target precise and lets the inventory span any subject. — For Mova (frames): speech-act and attitude frames could record which proposition (a frame node) a speaker supports, refutes or questions, not merely the topic.
  • I2. Split each stance into explicit versus implicit subcategories, so that hedged or sarcastic leaning is labeled separately from outright assertion. — For Mova (frames): implicature and irony modules could output an "implied stance" flag distinct from literal assertion, keeping the inference step explainable.
  • I3. Hold out whole claims, and separately train on international claims while testing on regional ones, to measure true generalization instead of memorized topics. — For Mova (learn): evaluation splits could hold out entire fables, topics or source regions as a stricter control against leakage.
  • I4. Collect tweets with hand-built keyword queries instead of semantic similarity, since similarity-based retrieval (as in CovidLies with BERTScore) over-samples supporting texts. — For Mova (learn): when mining silver data, avoid selecting examples by similarity to the target, or the sample will be biased toward agreeing cases.
  • I5. Use EasyAdapt-style domain feature augmentation so one model learns from two differently collected corpora without the usual cross-domain drop. — For Mova (tree): gparse could be trained jointly on several treebanks or UD dialects, with shared plus domain-specific copies of features, instead of naive concatenation.

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2210.15954 · arXiv 2210.15954

@misc{xu2022stanceosaurus,
  title = {{Stanceosaurus: Classifying Stance Towards Multilingual Misinformation}},
  author = {Xu;, Jonathan Zheng; Ashutosh Baheti; Tarek Naous; Wei and Ritter, Alan},
  year = {2022},
  doi = {10.48550/arxiv.2210.15954},
  eprint = {2210.15954},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2210.15954}
}

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