Paper Type

ERF

Abstract

This study presents a methodological demonstration of the Multiple Stream Framework by exploring media attention and framing of digital twins. The pilot study used a cross-sectional sample of 64 news articles published during 2025 by the ten most influential news outlets. The sample was human-coded using a priori coding into four categories: technology, problem, policy, and politics. The results identified the need to further refine the coding to clarify the scope of the regulatory alternatives, define the goals of advocacy agents, evaluate the different definitions of digital twins reported in the media, and identify positive or unknown impacts on society. After further testing, the refined code will be used to train a generative AI to perform human-machine coding of the full dataset of news published by the 500 most influential news outlets from 2002 to 2026.

Paper Number

1588

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SIG E-GOV

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Aug 15th, 12:00 AM

Regulating Digital Twins: Media Discourse

This study presents a methodological demonstration of the Multiple Stream Framework by exploring media attention and framing of digital twins. The pilot study used a cross-sectional sample of 64 news articles published during 2025 by the ten most influential news outlets. The sample was human-coded using a priori coding into four categories: technology, problem, policy, and politics. The results identified the need to further refine the coding to clarify the scope of the regulatory alternatives, define the goals of advocacy agents, evaluate the different definitions of digital twins reported in the media, and identify positive or unknown impacts on society. After further testing, the refined code will be used to train a generative AI to perform human-machine coding of the full dataset of news published by the 500 most influential news outlets from 2002 to 2026.