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A Survey on Stance Detection for Mis- and Disinformation Identification

Summary

Stance detection identifies the attitude a text takes toward a target. It underpins many systems that flag false content online, both misinformation (false but unintended) and disinformation (false and meant to harm). Earlier surveys addressed stance in general, on social media, or in rumour verification, but none examined how stance relates to false-information detection. This survey fills that gap.

The authors adopt the stance definition of Küçük and Can (2020) and separate stance from neighbouring tasks such as sentiment analysis and sarcasm detection. They describe two ways stance is used:

  • As fact-checking itself: the stance of evidence toward a claim serves directly as the veracity label.
  • As one module in a larger pipeline: for example, rumour verification, misconception detection, or media profiling.

They catalogue English and non-English datasets, propose a four-level taxonomy of stance terminology, and review modelling approaches. They close with open challenges: graded truth labels, richer label semantics, explainability, and tighter integration of stance into fact-checking workflows. The paper is a practical map of resources and research directions for anyone building veracity-oriented NLP.

Key points

  • English datasets reviewed: Rumour Has It, PHEME, Emergent, FNC-1 (~75K), RumourEval '17/'19, FEVER (~185K), Snopes (~19.5K), COVIDLies, and TabFact (~118K, claims checked against Wikipedia tables).
  • Non-English resources are few and small (roughly 0.9K–4K instances): Arabic FC, ANS, AraStance (Arabic), DAST (Danish), and a Croatian dataset.
  • Label schemes differ by task:
    • FNC-1 relates article bodies to headlines with four labels, including discuss and unrelated.
    • FEVER uses SUPPORTED / REFUTED / NOT ENOUGH INFO and requires multi-hop reasoning.
  • Four-level taxonomy: the proposed scheme covers data sources, inputs (target plus context), meta-level categorisation, and textual object types.
  • Future directions:
    • finer-grained labels that capture stance strength;
    • better transfer across datasets with mismatched labels;
    • realistic explanations, since attention weights or FEVER-style artificial data are not enough;
    • weighting crowd stance by source credibility.

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

Cited by

62 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 56 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.findings-naacl.94 · arXiv 2103.00242

@inproceedings{hardalov2022survey,
  title = {{A Survey on Stance Detection for Mis- and Disinformation Identification}},
  author = {Hardalov, Momchil and Arora, Arnav and Nakov, Preslav and Augenstein, Isabelle},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-naacl.94},
  eprint = {2103.00242},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.findings-naacl.94}
}

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