Uncovering Bias: Leveraging Large Language Models for News Analysis inthe Context of Palestine

Abstract

The Israel-Palestine conflict is one on which public opinion is greatly influenced by media bias. Detecting and understanding such bias in news reporting is necessary to promote transparency and accountability in journalism. The issue of media bias detection using advanced deep learning techniques is addressed in this research, particularly transformer-based architectures such as Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), Bidirectional Encoder Representations from Transformers Mini (BERT-Mini), Compact Bidirectional Encoder Representations from Transformers (TinyBERT). A dataset of 9,000 news articles is used, labeled with sentiment using the TextBlob library, with sentiment serving as a measurable proxy for the emotional framing dimension of media bias. We evaluate the performance of these transformer models against more traditional deep learning architectures consisting of Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). We find that transformer-based models greatly outperform their sequential counterparts. The best performance was achieved by DistilBERT with 94% accuracy, 89% precision, 86% recall and F1 score of 87%, verifying that DistilBERT possesses the best capability to capture subtle contextual nuances which specify media bias. In this study, we demonstrate the potential of lightweight transformer-based language models to enable bias detection in digital journalism with a scalable and consistent approach. Finally, our findings are situated in the growing field of automated media bias profiling, and we call for further work to extend the use of machine learning for promoting fair, transparent news coverage. Future work should combine multilingual and multimodal data to get a more complete sense of bias over different media landscapes.