The MediaSpin Dataset: Post-Publication News Headline Edits Annotated for Media Bias

TL;DR

MediaSpin is a large dataset capturing how major news outlets modify headlines post-publication, enabling analysis of media bias, framing, and social media engagement through annotated headline pairs and downstream applications.

Contribution

This work introduces MediaSpin, a novel large-scale dataset with annotated headline edits for media bias, and demonstrates its utility in bias classification and behavioral analysis.

Findings

Regional differences in headline framing and bias.

Measurable linguistic markers of bias.

Higher engagement with biased headlines.

Abstract

We present MediaSpin, a large-scale language resource capturing how major news outlets modify headlines after publication, and MediaSpin-in-the-Wild, a complementary dataset linking these revised headlines to their downstream engagement on social media. The increasing editability of online news headlines offers new opportunities to study linguistic framing and bias through the lens of editorial revisions. The dataset contains 78,910 headline pairs annotated for 13 types of media bias, grounded in established media-bias taxonomies, covering both subjective (e.g., sensationalism, spin) and objective (e.g., omission, slant) forms, with annotation conducted through a human-supervised large-language-model pipeline with expert validation and quality control. We describe the annotation schema and demonstrate three downstream applications: (1) cross-national analysis of how country references are added or removed during editing, (2) transformer-based bias classification at both binary and fine-grained levels, and (3) behavioral analysis of biased headlines on X (Twitter) using 180,786 news-related tweets from 819 consenting users. The results reveal regional asymmetries in representational framing, measurable linguistic markers, and consistently higher engagement with biased content. MediaSpin and MediaSpin-in-the-Wild together provide a reproducible benchmark for bias detection and the study of editorial and behavioral dynamics in contemporary media ecosystems.