A Deep Learning Framework for Classifying and Mitigating Bias in News Reporting

The media plays a significant role in shaping public perception, but media bias can distort this influence, often seen in one-sided or polarizing content, particularly in word choice. Such bias not only distorts public discourse but also challenges information integrity, especially with news spreading rapidly on social media. Our research aims to address the limitations of current bias detection models and manual classification inefficiencies. We focus on identifying and counteracting bias to promote unbiased reporting. Our study primarily aims to classify news biases into left, right, and center perspectives while mitigating biases for impartial reporting. We introduce a novel approach using a Transformer-based deep learning framework enhanced by multi-task learning techniques. Our methodology includes advanced deep learning models, notably GPT-2, fine-tuned for superior performance in metrics like BLEU and ROUGE scores. To support our model’s efficacy, we developed a unique dataset tailored to our research objectives. Our findings represent a significant stride towards achieving accuracy and fairness in news reporting, signaling a new era in digital media integrity.