Political speech is an active area of investigation and the ongoing ERC project Explaining Individual Lifespan Change (EILisCh) expands on some of the previous findings in this area. Previous work has found that political speech can differ based on party membership in a time-wise static environment and it has also been uncovered that individual politicians can change their linguistic behavior over time. In this paper, we pursue a novel topic in this area, the evolution of language use of entire political parties over time. We focus on Icelandic political parties and their use of Stylistic Fronting from 1999 to 2021, with a particular emphasis on the years around the financial crisis of 2008, and the subsequent years. Our results show that parties in a position of power typically speak more formally, using more Stylistic Fronting, but that at the same time there are some exceptions to this pattern. We highlight the significance of relying on a large speech corpus, when applying a high-definition approach to linguistic analyses across time.

This paper examines machine bias in language technology. Machine bias can affect machine learning algorithms when language models trained on large corpora include biased human decisions or reflect historical or social inequities, e.g. regarding gender and race. The focus of the paper is on gender bias in machine translation and we discuss a study conducted on Icelandic translations in the translation systems Google Translate and Vélþýðing.is. The results show a pattern which corresponds to certain societal ideas about gender. For example it seems to depend on the meaning of adjectives referring to people whether they appear in the masculine or feminine form. Adjectives describing positive personality traits were more likely to appear in masculine gender whereas the negative ones frequently appear in feminine gender. However, the opposite applied to appearance related adjectives. These findings unequivocally demonstrate the importance of being vigilant towards technology so as not to maintain societal inequalities and outdated views — especially in today’s digital world.