The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data

TLDR

Traditional research on communication dynamics relies on surveys and experiments, yet suffers from self‑report distortion; digital exposure and expressive behaviors such as tweets now offer direct data that could reshape communication theory. The study aims to use big data to examine attention and framing across traditional and social media for 29 political issues in 2012. The authors analyze large‑scale digital traces to assess how attention and framing unfold across traditional and social media. They find that agenda setting is a complex, dynamic interaction rather than a one‑way flow from traditional media to audiences, with attention rhythms in each medium responding to distinct drivers.

Abstract

Researchers have used surveys and experiments to better understand communication dynamics, but confront consistent distortion from self-report data. But now both digital exposure and resulting expressive behaviors (such as tweets) are potentially accessible for direct analysis with important ramifications for the formulation of communication theory. We utilize "big data" to explore attention and framing in the traditional and social media for 29 political issues during 2012. We find agenda setting for these issues is not a one-way pattern from traditional media to a mass audience, but rather a complex and dynamic interaction. Although the attentional dynamics of traditional and social media are correlated, evidence suggests that the rhythms of attention in each respond to a significant degree to different drummers.