Last September, Jaya Caporusso obtained a Master’s degree in Information and Communication Technologies from the Jožef Stefan International Postgraduate School. She successfully defended her thesis, titled “Computational Analysis of Socially Biased and Dehumanising Discourse“, conducted under the supervision of Asst. Prof. Dr. Senja Pollak and Prof. Dr. Matthew Purver. She is now a Young Researcher at the Department of Knowledge Technologies.

Congratulations!

Abstract:

This thesis presents a computational analysis of socially biased and dehumanising discourse in Slovene news media, using natural language processing techniques. Social biases are not only reflected, but also perpetuated in language, and dehumanising discourse both results from and results in a discriminative perception and treatment of a specific social group. Specifically, we focus on the discourse on migrants in Slovene news media in the migration periods following the wars in Syria (2015-2016) and in Ukraine (2022-2023), and on the
representation of migrants and members of the LGBTQIA+ community in news media consumed by a left-,centre, or right-wing-leaning public.

The main contribution of the thesis is a novel adaptation and application of natural language processing techniques to the detection of social bias and dehumanisation in Slovene. The approaches employed include the training of static word embeddings, vector similarity, sentiment analysis, and masked token prediction.

The results of the empirical studies reveal that Slovene news articles about migrants are generally more negative, intense, and dehumanising during the migration period following the war in Ukraine compared to the period following the war in Syria. For instance, migrants are more closely associated with concepts such as moral disgust and vermin during the period of the Ukrainian war compared to the Syrian war. However, when comparing the articles about Ukrainian migrants and other migrants in the 2022-2023 period, the
ones specifically referring to Ukrainian migrants are more positive, more intense, and less dehumanising than those referring to other migrants. Furthermore, female migrants and female members of the LGBTQIA+ community face higher levels of dehumanisation in
media outlets consumed by a right-wing public compared to those read by a centrist or left-wing audience.

The results emphasise the urgent need for continued research to address the harmful effects of socially-biased and dehumanising language in news media.