新聞媒體敘事的FIGNEWS共享任務

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

我們介紹了 FIGNEWS 共享任務的概況，該任務作為與 ACL 2024 同期舉辦的 ArabicNLP 2024 會議的一部分。這個共享任務討論了多語言新聞帖子中的偏見和宣傳標註。我們以加薩以色列戰爭初期作為案例研究。該任務旨在通過創建分析不同敘述的框架，突顯潛在的偏見和宣傳，促進合作發展主觀任務的標註指南。我們以多語言的角度來處理這個問題，具體來說是在五種語言中：英語、法語、阿拉伯語、希伯來語和印地語。共有17個團隊參與了兩個標註子任務：偏見（16個團隊）和宣傳（6個團隊）。這些團隊參加了四個評估軌跡：指南開發、標註質量、標註數量和一致性。總共，這些團隊產生了129,800個數據點。討論了關鍵發現和對該領域的影響。

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.