A Temporal Multipartite Network for News Articles Insights Extraction

In the modern era, news articles are pivotal in shaping public perception and disseminating information. However, the neutrality of these articles is often compromised by inherent biases from both sources and authors. Such biases can distort readers’ understanding of events, leading to misinformation and societal discord. With the rise of artificial intelligence, new opportunities have emerged to address these challenges. This study leverages network science to analyze media bias, introducing a novel framework that deciphers the complex relationships among articles, publishers, and authors. Our model integrates multiple components for comprehensive information extraction, which are then used to construct a multi-partite temporal network. This network captures the dynamic interactions within the media ecosystem, allowing for detailed analyses of temporal patterns, information flow, and key influencers. By employing this AIdriven approach, we provide a robust methodology for exploring and understanding the intricate dynamics of media bias, thereby contributing to a more balanced and trustworthy information landscape.