A political cartography of news sharing: Capturing story, outlet and content level of news circulation on Twitter

2025-05-13

AI Summary

Existing political analyses of news sharing on digital platforms suffer from three key limitations: oversimplified measurement of user political orientation, exclusive focus on media organizations rather than content or sources, and reliance on a binary partisan framework—thus failing to capture the multidimensional heterogeneity of news sources and narratives. This study proposes a multilayered mapping framework for politicized news sharing that transcends unidimensional ideological labels. It jointly models source-level attributes, story-level narrative content, and users’ two-dimensional political orientation (left–right and populist–establishment axes). Leveraging Twitter tracking data, we integrate NLP, multidimensional political measurement, and diffusion network analysis. Key findings reveal: (1) users exhibit significant cross-ideological news diversity along the second dimension; (2) media outlets tailor content distribution differentially across political segments; and (3) inter-media cross-ideological sharing intensifies for specific issue domains.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

News sharing on digital platforms shapes the digital spaces millions of users navigate. Trace data from these platforms also enables researchers to study online news circulation. In this context, research on the types of news shared by users of differential political leaning has received considerable attention. We argue that most existing approaches (i) rely on an overly simplified measurement of political leaning, (ii) consider only the outlet level in their analyses, and/or (iii) study news circulation among partisans by making ex-ante distinctions between partisan and non-partisan news. In this methodological contribution, we introduce a research pipeline that allows a systematic mapping of news sharing both with respect to source and content. As a proof of concept, we demonstrate insights that otherwise remain unnoticed: Diversification of news sharing along the second political dimension; topic-dependent sharing of outlets; some outlets catering different items to different audiences.

Problem

• Analyzing news sharing on Twitter across story, outlet, and content levels
• Addressing oversimplified political leaning measurements in existing research
• Exploring topic-dependent outlet sharing and audience-specific content delivery

Innovation

• Systematic mapping of news sharing sources and content
• Multi-level analysis of news circulation on Twitter
• Topic-dependent sharing patterns and audience targeting