mlsanityData to 7 Oct 2026

A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media

arXiv 2404.07036 ↗First posted 10 Apr 2024cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 47 references that are in the map. First posted on arXiv 10 Apr 2024.

Builds on

13 of 47 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  7. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  8. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 294
  9. 2013Crowdsourcing a Word-Emotion Association Lexiconcited by 193
  10. 2014Temporal Analysis of Language through Neural Language Modelscited by 53
  11. 2020A Framework for the Computational Linguistic Analysis of Dehumanizationcited by 31
  12. 2020Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexiconscited by 17
1 more
  1. 2023Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformerscited by 11

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Unveiling Affective Polarization Trends in Parliamentary Proceedingscited by 1