关于新闻媒体叙事的FIGNEWS共享任务

The FIGNEWS Shared Task on News Media Narratives

July 25, 2024

作者: Wajdi Zaghouani, Mustafa Jarrar, Nizar Habash, Houda Bouamor, Imed Zitouni, Mona Diab, Samhaa R. El-Beltagy, Muhammed AbuOdeh

cs.AI

摘要

我们介绍了FIGNEWS共享任务的概述，该任务作为ArabicNLP 2024会议的一部分与ACL 2024同期举办。该共享任务致力于处理多语言新闻帖子中的偏见和宣传标注。我们以加沙以色列战争初期为案例进行研究。该任务旨在促进协作，通过创建分析不同叙事的框架，突出潜在的偏见和宣传，制定主观任务的标注指南。秉持促进和鼓励多样性的精神，我们从多语言的角度来解决这一问题，即在五种语言中：英语、法语、阿拉伯语、希伯来语和印地语。共有17个团队参与了两个标注子任务：偏见（16个团队）和宣传（6个团队）。团队参加了四个评估轨道的竞争：指南开发、标注质量、标注数量和一致性。总体而言，这些团队共产生了129,800个数据点。讨论了关键发现和对该领域的影响。

English

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.