# Stance Prediction and Claim Verification: An Arabic Perspective

arXiv: https://arxiv.org/abs/2005.10410 · HTML: https://mlsanity.com/paper/2005.10410/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 25 of 25)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [ORCA: A Challenging Benchmark for Arabic Language Understanding](https://mlsanity.com/paper/2212.10758/) (2022)
- [CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking](https://mlsanity.com/paper/2206.11863/) (2022)
- [GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://mlsanity.com/paper/2305.14976/) (2023)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [LAraBench: Benchmarking Arabic AI with Large Language Models](https://mlsanity.com/paper/2305.14982/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Native vs Non-Native Language Prompting: A Comparative Analysis](https://mlsanity.com/paper/2409.07054/) (2024)
- [AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking](https://mlsanity.com/paper/2104.13559/) (2021)
- [Beyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking](https://mlsanity.com/paper/2502.15419/) (2025)
- [LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content](https://mlsanity.com/paper/2410.15308/) (2024)
- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP](https://mlsanity.com/paper/2506.08768/) (2025)
- [Give Me More Details: Improving Fact-Checking with Latent Retrieval](https://mlsanity.com/paper/2305.16128/) (2023)
- [ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source](https://mlsanity.com/paper/2405.07615/) (2024)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis](https://mlsanity.com/paper/2411.04604/) (2024)
- [Mawqif-XT: An Arabic Benchmark Dataset for Cross-Target Stance Detection](https://mlsanity.com/paper/2608.09539/) (2026)
- [Detecting Stance of Authorities towards Rumors in Arabic Tweets: A Preliminary Study](https://mlsanity.com/paper/2301.05863/) (2023)
- [On the importance of Data Scale in Pretraining Arabic Language Models](https://mlsanity.com/paper/2401.07760/) (2024)
- [LASTIST: LArge-Scale Target-Independent STance dataset](https://mlsanity.com/paper/2510.25783/) (2025)
- [CritiSense: Critical Digital Literacy and Resilience Against Misinformation](https://mlsanity.com/paper/2603.16672/) (2026)
- [E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets](https://mlsanity.com/paper/2609.11334/) (2026)

## Abstract (arXiv)

This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.