An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles
This work addresses the challenge of media bias detection for news readers, but it is incremental as it builds on existing methods like BERT and distant supervision.
The paper tackles the problem of automatically detecting media bias in news articles by addressing the lack of gold-standard datasets and high context dependencies, with initial results showing the effectiveness of an interdisciplinary approach using NLP and deep learning, where a BERT-based model pre-trained on distant labels indicates potential for bias detection.
Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of gold-standard data sets and high context dependencies. In this research project, I aim to devise data sets and methods to identify media bias. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from psychology and linguistics. The first results indicate the effectiveness of an interdisciplinary research approach. My vision is to devise a system that helps news readers become aware of media coverage differences caused by bias. So far, my best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels, indicating that distant supervision has the potential to become a solution for the difficult task of bias detection.