MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines

Preetika Verma, Kokil Jaidka
12/3/2024
cs.CL

No confident code implementation found for this paper. 3 possible matches can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Decoding News Bias: Multi Bias Detection in News Articles

85% match2025

Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts

84% match2 repos92025

Frame In, Frame Out: Do LLMs Generate More Biased News Headlines than Humans?

84% match2025

The Table of Media Bias Elements: A sentence-level taxonomy of media bias types and propaganda techniques

83% match2026

Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias

82% match2025

Intertwined Biases Across Social Media Spheres: Unpacking Correlations in Media Bias Dimensions

82% match2024

Cite this paper

@article{verma2024mediaspin,
  title  = {MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines},
  author = {Preetika Verma and Kokil Jaidka},
  year   = {2024},
  eprint = {2412.02271},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.02271v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2412.02271.svg)](https://codeofpaper.com/paper/2412.02271)

Discussion