Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societ al shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.
@cite_7: Preface to the Third Edition Acknowledgements PART ONE: INTRODUCTORY 1 Language and gender 2 The historical background (I) - Folklinguistics and the early grammarians 3 The historical background (II) - Anthropologists and dialectologists PART TWO: THE SOCIOLINGUISTIC EVIDENCE 4 Quantitative studies 5Social networks 6 Gender differences in conversational practice 7 Conversational dominance in mixed talk 8 Same-sex talk PART THREE: CAUSES AND CONSEQUENCES 9 Children and gender-differentiated language 10 The role of gender differences in linguistic change 11The social consequences of gender differences in language PART FOUR: LOOKING TO THE FUTURE 12 New developments in language and gender research Bibliography Index
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@cite_1: Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change. The first measure, which has been used in many previous works, analyzes global shifts in a word's distributional semantics; it is sensitive to changes due to regular processes of linguistic drift, such as the semantic generalization of promise ("I promise." "It promised to be exciting."). The second measure, which we develop here, focuses on local changes to a word's nearest semantic neighbors; it is more sensitive to cultural shifts, such as the change in the meaning of cell ("prison cell" "cell phone"). Comparing measurements made by these two methods allows researchers to determine whether changes are more cultural or linguistic in nature, a distinction that is essential for work in the digital humanities and historical linguistics.
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@cite_2: The topics of race and racism are often treated narrowly in social psychological and other social scientific literature, usually being presented as subcategories of stereotyping or prejudice or attitudes. In this new book, Margaret Wetherell and Jonathan Potter extend their work on the use of discourse analysis in social science to cover racism and to include issues of social structure, power relations and ideology. Part 1 provides the theoretical framework within which representations of race can be studied, part 2 an empirical illustration from New Zealand of the arguments of part 1.
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@cite_10: We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track its linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book Ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.
@cite_11: Basow continues to present a balanced view of the literature on both men and womens gender roles, with thorough attention to the empirical research. In a field that is generating research at an amazing pace, Basow provides the most comprehensive, most up-to-date and most research-oriented book available, presenting all the current findings in psychology and sociology, as well as biology, political science, and anthropology. She covers both the "old" topics related to gender as well as new concerns in the field, such as AIDS and data on changing families.
The study of gender and ethnic stereotypes is a large focus in linguistics and sociology, and is too extensive to be surveyed here @cite_6 @cite_11 @cite_2 @cite_0 @cite_7 . Our main innovation is the use of word embeddings which provides a new lens to measure and quantify biases. Another related field in linguistics studies how language changes over time and has also recently employed word embeddings as a tool @cite_5 @cite_8 @cite_10 . However, this literature primarily studies changes, such as how the word used to primarily mean and now means predominantly means @cite_1 @cite_3 , and does not investigate bias.