# The FIGNEWS Shared Task on News Media Narratives

arXiv: https://arxiv.org/abs/2407.18147 · HTML: https://mlsanity.com/paper/2407.18147/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [Towards A Reliable Ground-Truth For Biased Language Detection](https://mlsanity.com/paper/2112.07421/) (2021)
- [Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy](https://mlsanity.com/paper/2304.02247/) (2023)
- [Offensive Hebrew Corpus and Detection using BERT](https://mlsanity.com/paper/2309.02724/) (2023)
- [WojoodNER 2024: The Second Arabic Named Entity Recognition Shared Task](https://mlsanity.com/paper/2407.09936/) (2024)
- [TASSY -- A Text Annotation Survey System](https://mlsanity.com/paper/2112.07391/) (2021)
- [Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study](https://mlsanity.com/paper/2310.14032/) (2023)
- [Check News in One Click: NLP-Empowered Pro-Kremlin Propaganda Detection](https://mlsanity.com/paper/2401.15717/) (2024)
- [Sina at FigNews 2024: Multilingual Datasets Annotated with Bias and Propaganda](https://mlsanity.com/paper/2407.09327/) (2024)
- [Target-Aware Contextual Political Bias Detection in News](https://mlsanity.com/paper/2310.01138/) (2023)

## Built on by (most cited first, 7 of 7)

- [QAEncoder: Towards Aligned Representation Learning in Question Answering Systems](https://mlsanity.com/paper/2409.20434/) (2024)
- [StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse](https://mlsanity.com/paper/2606.12068/) (2026)
- [Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation](https://mlsanity.com/paper/2503.23242/) (2025)
- [Beyond the Battlefield: Framing Analysis of Media Coverage in Conflict Reporting](https://mlsanity.com/paper/2506.10421/) (2025)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)
- [Multimodal Analysis of State-Funded News Coverage of the Israel-Hamas War on YouTube Shorts](https://mlsanity.com/paper/2604.00994/) (2026)
- [When Similar Means Different: Evaluating LLMs on Arabic--Hebrew Cognates](https://mlsanity.com/paper/2606.13218/) (2026)

## Abstract (arXiv)

We present an overview of the FIGNEWS shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. The shared task addresses bias and propaganda annotation in multilingual news posts. We focus on the early days of the Israel War on Gaza as a case study. The task aims to foster collaboration in developing annotation guidelines for subjective tasks by creating frameworks for analyzing diverse narratives highlighting potential bias and propaganda. In a spirit of fostering and encouraging diversity, we address the problem from a multilingual perspective, namely within five languages: English, French, Arabic, Hebrew, and Hindi. A total of 17 teams participated in two annotation subtasks: bias (16 teams) and propaganda (6 teams). The teams competed in four evaluation tracks: guidelines development, annotation quality, annotation quantity, and consistency. Collectively, the teams produced 129,800 data points. Key findings and implications for the field are discussed.