使用词嵌入研究1950-1990年荷兰报纸中的性别偏见
Using Word Embeddings to Examine Gender Bias in Dutch Newspapers, 1950-1990
摘要：
当代关于公共和社交媒体中的过滤泡沫和两极分化的辩论提出了一个问题，即过去的新闻媒体在多大程度上表现出偏见。本文专门探讨偏见有关的性别在1950年和1990年之间的六个荷兰全国性报纸。我们通过比较在具有不同意识形态背景的报纸上训练的词嵌入模型的局部变化来衡量与性别相关的偏见。我们展示了性别偏见的明显差异，以及随着时间的推移报纸内部和之间的变化。在性和休闲等主题方面，我们看到偏向女性的倾向，而一般来说，偏向男性的倾向，尽管女性就业人数和女权主义运动不断增加。尽管荷兰社会在意识形态上变得不那么分层（去支柱化），但我们发现宗教和社会民主派与自由派报纸之间的性别偏见越来越大。在方法论上，本文阐述了如何利用词嵌入来考察历史语言变化。未来的工作将研究如何微调深度上下文嵌入模型，如ELMO，可能用于具有更多上下文信息的类似任务。
英文摘要：
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.
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