Word meanings change over time. Detecting shifts in meaning for particular words has been the focus of much research recently. We address the complementary problem of monitoring shifts in vocabulary over time. That is, given a small seed set of words, we are interested in monitoring which terms are used over time to refer to the underlying concept denoted by the seed words.In this paper, we propose an algorithm for monitoring shifts in vocabulary over time, given a small set of seed terms. We use distributional semantic methods to infer a series of semantic spaces over time from a large body of time-stamped unstructured textual documents. We construct semantic networks of terms based on their representation in the semantic spaces and use graph-based measures to calculate saliency of terms. Based on the graph-based measures we produce ranked lists of terms that represent the concept underlying the initial seed terms over time as final output.As the task of monitoring shifting vocabularies over time for an ad hoc set of seed words is, to the best of our knowledge, a new one, we construct our own evaluation set. Our main contributions are the introduction of the task of ad hoc monitoring of vocabulary shifts over time, the description of an algorithm for tracking shifting vocabularies over time given a small set of seed words, and a systematic evaluation of results over a substantial period of time (over four decades). Additionally, we make our newly constructed evaluation set publicly available.

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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. 2 The digitized newspapers were provided by the National Library of the Netherlands.

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Industry and governments have deployed computer vision models to make high-stake decisions in society. While they are often presented as neutral and objective, scholars have recognized that bias in these models might lead to the reproduction of racial, social, cultural and economic inequity. A growing body of work situates the provenance of bias in the collection and annotation of datasets that are needed to train computer vision models. This article moves from studying bias in computer vision models to the agency that is commonly attributed to them: the fact that they are universally seen as being able to make biased decisions. Building on the work of Bruno Latour and Jonathan Crary, the authors discuss computer vision models as agential optical instruments in the production of contemporary visuality. They analyse five interconnected research steps – task selection, category selection, data collection, data labelling and evaluation – of six widely cited benchmark datasets, published during a critical stage in the development of the field (2004–2020): Caltech 101, Caltech 256, PASCAL VOC, ImageNet, MS COCO and Google Open Images. They found that, despite all sorts of justifications, the selection of categories is not based on any general notion of visuality, but depends heavily upon perceived practical applications, the availability of downloadable images and, in conjunction with data collection, favours categories that can be unambiguously described by text. Second, the reliance on Flickr for data collection introduces a temporal bias in computer vision datasets. Third, by comparing aggregate accuracy rates and ‘human’ performance, the dataset papers introduce a false dichotomy between the agency of computer vision models and human observers. In general, the authors argue that the agency of datasets is produced by obscuring the power and subjective choices of its creators and the countless hours of highly disciplined labour of crowd workers.