# Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990

arXiv: https://arxiv.org/abs/1907.08922 · HTML: https://mlsanity.com/paper/1907.08922/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Gender Bias Hidden Behind Chinese Word Embeddings: The Case of Chinese Adjectives](https://mlsanity.com/paper/2106.00181/) (2021)
- [Machine-Learning media bias](https://mlsanity.com/paper/2109.00024/) (2021)
- [Measuring Intersectional Biases in Historical Documents](https://mlsanity.com/paper/2305.12376/) (2023)
- [An Analytics of Culture: Modeling Subjectivity, Scalability, Contextuality, and Temporality](https://mlsanity.com/paper/2211.07460/) (2022)
- [Computational modeling of semantic change](https://mlsanity.com/paper/2304.06337/) (2023)
- [Data Bias According to Bipol: Men are Naturally Right and It is the Role of Women to Follow Their Lead](https://mlsanity.com/paper/2404.04838/) (2024)
- [Combining Objective and Subjective Perspectives for Political News Understanding](https://mlsanity.com/paper/2408.11174/) (2024)
- [Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures](https://mlsanity.com/paper/2603.17952/) (2026)
- [ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science](https://mlsanity.com/paper/2310.20328/) (2023)

## Abstract (arXiv)

Contemporary debates on filter bubbles and polarization in public and social media raise the question to what extent news media of the past exhibited biases. This paper specifically examines bias related to gender in six Dutch national newspapers between 1950 and 1990. We measure bias related to gender by comparing local changes in word embedding models trained on newspapers with divergent ideological backgrounds. We demonstrate clear differences in gender bias and changes within and between newspapers over time. In relation to themes such as sexuality and leisure, we see the bias moving toward women, whereas, generally, the bias shifts in the direction of men, despite growing female employment number and feminist movements. Even though Dutch society became less stratified ideologically (depillarization), we found an increasing divergence in gender bias between religious and social-democratic on the one hand and liberal newspapers on the other. Methodologically, this paper illustrates how word embeddings can be used to examine historical language change. Future work will investigate how fine-tuning deep contextualized embedding models, such as ELMO, might be used for similar tasks with greater contextual information.