SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles
Dilshod Azizov, Zain Muhammad Mujahid, Hilal AlQuabeh, Preslav Nakov, Shangsong Liang
Abstract
In an era where information is quickly shared across many cultural and language contexts, the neutrality and integrity of news media are essential. Ensuring that media content remains unbiased and factual is crucial for maintaining public trust. With this in mind, we introduce SAFARI (CroSs-lingual BiAs and Factuality Detection in News MediA and News ARtIcles), a novel corpus of news media and articles for predicting political bias and the factuality of reporting in a multilingual and cross-lingual setup. To the best of our knowledge, this corpus is unprecedented in its collection and introduces a dataset for political bias and factuality for three tasks: (i) media-level, (ii) article-level, and (iii) joint modeling at the article-level. At the media and article levels, we evaluate the cross-lingual ability of the models; however, in joint modeling, we evaluate on English data. Our frameworks set a new benchmark in the cross-lingual evaluation of political bias and factuality. This is achieved through the use of various Multilingual Pre-trained Language Models (MPLMs) and Large Language Models (LLMs) coupled with ensemble learning methods.
BibTeX
@inproceedings{azizov-etal-2024-safari,
title = "{SAFARI}: Cross-lingual Bias and Factuality Detection in News Media and News Articles",
author = "Azizov, Dilshod and
Mujahid, Zain Muhammad and
AlQuabeh, Hilal and
Nakov, Preslav and
Liang, Shangsong",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.712/",
doi = "10.18653/v1/2024.findings-emnlp.712",
pages = "12217--12231"
}