Tailoring the message: A new dataset on the dyadic nature of NGO shaming in the media

Abstract

In the last decade, international relations scholarship on shaming by non-governmental organizations has grown dramatically, providing us with many insights into how country-level improvement occurs in the areas of human rights and the environment, among other issues. Using machine learning techniques, this project built an updated dataset on NGO shaming from almost 1.5 million articles in the media from 2001 to 2020. The dataset covers a wide set of organizations with goals outside of the traditional focus on human rights. Our approach will allow researchers to explicitly examine shaming as a dyadic event, occurring from a specific NGO sender to a specific country target. Using our new dataset, we first validated existing research on shaming in the current populist era and then examined how the nature of the NGO and the nature of the country jointly facilitated shaming. Our approach and dataset will be useful to both academics and to the policy community.