---
title: "Integrating Stance Detection and Fact Checking — Baly 2018"
description: "Automatic fact checking chains several subtasks: finding relevant documents, judging each document's stance toward a claim, and aggregating those stances…"
authors: ["Ramy Baly", "Mitra Mohtarami", "James Glass", "Lluís Màrquez", "Alessandro Moschitti", "Preslav Nakov"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)"
source: "https://aclanthology.org/N18-2004/"
doi: "10.18653/v1/n18-2004"
arxiv: "1804.08012"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/baly-2018-integrating-stance-detection-fact-checking/"
---
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# Integrating Stance Detection and Fact Checking in a Unified Corpus

**Authors:** [Ramy Baly](https://md.research.elamur.ai/free/authors/ramy-baly/), [Mitra Mohtarami](https://md.research.elamur.ai/free/authors/mitra-mohtarami/), [James Glass](https://md.research.elamur.ai/free/authors/james-glass/), [Lluís Màrquez](https://md.research.elamur.ai/free/authors/lluis-marquez/), [Alessandro Moschitti](https://md.research.elamur.ai/free/authors/alessandro-moschitti/), [Preslav Nakov](https://md.research.elamur.ai/free/authors/preslav-nakov/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-2004/) · [PDF](https://aclanthology.org/N18-2004.pdf) · [DOI 10.18653/v1/n18-2004](https://doi.org/10.18653/v1/n18-2004) · [arXiv 1804.08012](https://arxiv.org/abs/1804.08012) · [OpenAlex](https://openalex.org/W2798329932)

**Citations:** 126 citations (openalex-snapshot · 2026-10-14) · [60](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Automatic fact checking chains several subtasks: finding relevant documents, judging each document's stance toward a claim, and aggregating those stances into a verdict. Earlier datasets treat these pieces separately and are mostly English. Baly et al. build the first Arabic fact-checking corpus that links them. False claims come from VERIFY, a site that debunks misinformation about the Syrian war. True claims come from Arabic REUTERS headlines on matching topics. For every claim, documents were retrieved from the open Web through the Google custom search API. Crowd workers then labeled each claim–document pair for stance using the FNC scheme. They also highlighted the text spans (rationales) behind agree and disagree judgments. Several FNC stance systems performed much worse on this corpus than on FNC. The authors attribute this to retrieval yielding realistic, topically close documents rather than trivially unrelated ones. Giving models gold rationales clearly improved stance detection and a simple stance-aggregation fact-checker. The work offers a template for jointly annotated, explainable fact-checking data beyond English.

## Key points
- Filtering 1,381 candidate claims left 422 verifiable ones: 219 false (VERIFY) and 203 true (REUTERS). These are paired with 3,042 retrieved documents (1,239 for false claims, 1,803 for true ones).
- Stance labels are agree / disagree / discuss / unrelated, collected via CrowdFlower from 3–5 annotators per pair. Annotators were screened on a hidden 50-pair set with a 75% accuracy cutoff, and labels were aggregated by majority vote.
- Models tested were the FNC baseline, Athene, UCL and a Memory Network, using 5-fold cross-validation with claims kept within a single fold. The FNC baseline reaches 75.2 weighted accuracy on FNC but only 55.6 on this corpus.
- Combining full documents with gold rationales gave the best results: up to 65.8 weighted accuracy (Memory Network) and 80.2% accuracy (Athene). These are upper bounds, since gold rationales were used at test time.
- A fact-checker that aggregates predicted stances scored 56.2 accuracy with full documents and 59.7 with best sentence + rationale, against a 50.5 majority baseline. The corpus is freely available from MIT CSAIL and QCRI.

*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](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

60 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 52 not yet.

- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://md.research.elamur.ai/free/papers/augenstein-2019-multifc-real-world-multi-domain/)
- [STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval](https://md.research.elamur.ai/free/papers/conforti-2020-stander-expert-annotated-dataset-news/)
- [The Truth, The Whole Truth, and Nothing but the Truth: A New Benchmark Dataset for Hebrew Text Credibility Assessment](https://md.research.elamur.ai/free/papers/findings-2023-hagag-truth-whole-truth-nothing-but/)
- [A Survey on Automated Fact-Checking](https://md.research.elamur.ai/free/papers/guo-2022-survey-automated-fact-checking/)
- [Cross-Domain Label-Adaptive Stance Detection](https://md.research.elamur.ai/free/papers/hardalov-2021-cross-domain-label-adaptive-stance/)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://md.research.elamur.ai/free/papers/hardalov-2022-survey-stance-detection-mis-disinformation/)
- [Automatic Stance Detection Using End-to-End Memory Networks](https://md.research.elamur.ai/free/papers/mohtarami-2018-automatic-stance-detection-end-end/)
- [Fact or Fiction: Verifying Scientific Claims](https://md.research.elamur.ai/free/papers/wadden-2020-fact-or-fiction-verifying-scientific/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{baly2018integrating,
  title = {{Integrating Stance Detection and Fact Checking in a Unified Corpus}},
  author = {Baly, Ramy and Mohtarami, Mitra and Glass, James and Màrquez, Lluís and Moschitti, Alessandro and Nakov, Preslav},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-2004},
  eprint = {1804.08012},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/n18-2004}
}
```

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