Ceasefire at FIGNEWS 2024 Shared Task: Automated Detection and Annotation of Media Bias Using Large Language Models
Year: 2024
Type: conference-paper
Abstract: In this paper, we present our approach for FIGNEWS Subtask 1, which focuses on detecting bias in news media narratives about the Israel war on Gaza.We used a Large Language Model (LLM) and prompt engineering, using GPT-3.5 Turbo API, to create a model that automatically flags biased news media content with 99% accuracy.This approach provides Natural Language Processing (NLP) researchers with a robust and effective solution for automating bias detection in news media narratives using supervised learning algorithms.Additionally, this paper provides a detailed analysis of the labeled content, offering valuable insights into media bias in conflict reporting.Our work advances automated content analysis and enhances understanding of media bias.
Authors: Noor Sadiah, Sara Al-Emadi, Sumaya Rahman
Institution: Hamad bin Khalifa University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402684066