A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media
This work addresses the problem of analyzing dehumanization in media for researchers and policymakers, though it is incremental as it adapts an existing method to a new language and context.
The study tackled the problem of measuring dehumanization in news media by adapting a computational linguistic approach to Slovene, finding that discourse became more negative and intense over time but was less dehumanizing for Ukrainian migrants compared to others.
Dehumanisation involves the perception and or treatment of a social group's members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sentiment resource, the use of zero-shot cross-lingual valence and arousal detection, and a new method for statistical significance testing. We then apply it to study attitudes to migration expressed in Slovene newspapers, to examine changes in the Slovene discourse on migration between the 2015-16 migration crisis following the war in Syria and the 2022-23 period following the war in Ukraine. We find that while this discourse became more negative and more intense over time, it is less dehumanising when specifically addressing Ukrainian migrants compared to others.