MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines
It addresses the issue of media bias shaping public perception for researchers and practitioners in media studies and NLP, but is incremental as it builds on existing bias analysis methods with a new dataset.
This study tackled the problem of identifying media bias in edited news headlines by introducing the MediaSpin dataset, which includes 78,910 headline pairs annotated with 13 bias types, enabling applications in bias prediction and user behavior analysis.
The editability of online news content has become a significant factor in shaping public perception, as social media platforms introduce new affordances for dynamic and adaptive news framing. Edits to news headlines can refocus audience attention, add or remove emotional language, and shift the framing of events in subtle yet impactful ways. What types of media bias are editorialized in and out of news headlines, and how can they be systematically identified? This study introduces the MediaSpin dataset, the first to characterize the bias in how prominent news outlets editorialize news headlines after publication. The dataset includes 78,910 pairs of headlines annotated with 13 distinct types of media bias, using human-supervised LLM labeling. We discuss the linguistic insights it affords and show its applications for bias prediction and user behavior analysis.