Learning Unbiased News Article Representation

Learning unbiased news article representations aims to create accurate and neutral computational models of news articles, overcoming biases inherent in both the data and existing algorithms. Current research focuses on incorporating external knowledge sources, leveraging temporal information and event context, and employing advanced architectures like graph neural networks and knowledge distillation to improve representation learning and downstream tasks such as personalized news recommendation and political leaning prediction. This work is crucial for mitigating misinformation, enhancing news recommendation systems, and fostering a more informed and balanced public discourse.