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

Summary

This methodological paper proposes a “political cartography” framework for studying how news circulates on Twitter, arguing that the dominant approach in partisan news-sharing research is too coarse to capture the actual political structure of circulation. The authors identify three recurring limitations in existing work: reliance on unidimensional (typically left–right) measures of political leaning, near-exclusive focus on the outlet as the unit of analysis, and overconcentration on a few national contexts (notably the US). In response, they develop a multi-level approach that maps circulation simultaneously at the level of individual stories, outlets, and content, paired with a richer characterization of the political positions of sharers.

Key Contributions

• Introduces a “political cartography” framework for mapping news circulation on social platforms.
• Argues for moving beyond unidimensional left–right scales toward a more nuanced (multi-dimensional) measure of sharer political leaning.
• Integrates story-level, outlet-level, and content-level analyses into a single analytical scheme.
• Offers a methodological critique of dominant trace-data approaches to partisan news sharing.

Methods

The approach uses Twitter trace data on news sharing and analyzes it across three nested levels — stories, outlets, and content. Sharers are positioned politically using a more nuanced measure than the conventional left–right axis, enabling the mapping of circulation patterns onto a richer political space. Specific datasets, sample sizes, and statistical techniques are not detailed in the available abstract.

Findings

• Substantive empirical findings are not specified in the available abstract; the paper’s principal payoff is the methodological framework itself.

Connections

This paper sits alongside other recent efforts to refine measurement of partisan media exposure and circulation on platforms, including audience-based outlet scaling and large-scale trace analyses such as Bakshy2015-rn and Bouchaud2026-lr. Its push to move beyond the outlet level toward story- and content-level circulation resonates with work on hyperpartisan and alternative news ecosystems like Rothut2026-or and Balluff2026-if, and its multi-dimensional take on political positioning connects to broader debates about polarization measurement in computational communication research, e.g. Bruns2025-fz and Yang2025-iv.