Examining power dynamics in global health governance using topic modeling and network analysis of Twitter data.

ABSTRACT:

Objectives

Despite increases in global health actors and funding levels, health inequities persist. We empirically tested whether global health governance (GHG) operates under the rational actor model (RAM) and characterised GHG power dynamics.

Design

We collected approximately 75 000 tweets of 20 key global health actors, between 2016 and 2020, using Twitter API. We generated priorities from tweets collected using topic modelling. Priorities from tweets were compared with stated priorities from content analyses of policy documents and with revealed priorities from network analyses of development assistance for health funding data. Comparing priorities derived from Twitter, policy documents and funding data, we can test whether GHG operates under RAM and characterise power d

SUBMITTER: Bermudez GF 

PROVIDER: S-EPMC9171232 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature