Dataset for studying gender disparity in English literary texts

Nagaraj, Akarsh; Kejriwal, Mayank

doi:10.1016/j.dib.2022.107905

Cited by 2 publications

(3 citation statements)

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“…Following this filtering, the final dataset comprised 2,443 texts that cover genres ranging from adventure and science fiction, to mystery and romance, and span the same publication period as the original corpus of 3,036 texts. Nagaraj and Kejriwal (2022) provide a summary of author metadata acquired by us through this manual process. Therein, we also provide details on the query that was used to acquire the original 3,036 texts from Project Gutenberg.…”

Section: Datamentioning

confidence: 99%

“…Although only publicly available packages have been used in the NLP-based pipeline described earlier, we recognize that the steps noted above are non-trivial to execute, especially by social scientists and digital humanities scholars who may want to use the data to run their own studies, or replicate the results described subsequently. Hence, we have published the processed version of the data as a peer-reviewed data-in-brief article, with links to a Mendeley repository containing the processed data (Nagaraj and Kejriwal, 2022). Also, code and data to replicate the specific experiments in this article are included as supplementary material.…”

Section: Descriptive Statistics and Availability Of Processed Data Fo...mentioning

confidence: 99%

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Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing

Kejriwal¹,

Nagaraj²

2023

Journal of Data Science

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Research has continued to shed light on the extent and significance of gender disparity in social, cultural and economic spheres. More recently, computational tools from the data science and Natural Language Processing (NLP) communities have been proposed for measuring such disparity at scale using empirically rigorous methodologies. In this article, we contribute to this line of research by studying gender disparity in 2,443 copyright-expired literary texts published in the pre-modern period, defined in this work as the period ranging from the beginning of the nineteenth through the early twentieth century. Using a replicable data science methodology relying on publicly available and established NLP components, we extract three different gendered character prevalence measures within these texts. We use an extensive set of statistical tests to robustly demonstrate a significant disparity between the prevalence of female characters and male characters in pre-modern literature. We also show that the proportion of female characters in literary texts significantly increases in female-authored texts compared to the same proportion in male-authored texts. However, regression-based analysis shows that, over the 120 year period covered by the corpus, female character prevalence does not change significantly over time, and remains below the parity level of 50%, regardless of the gender of the author. Qualitative analyses further show that descriptions associated with female characters across the corpus are markedly different (and stereotypical) from the descriptions associated with male characters.

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Section: Datamentioning

confidence: 99%

Section: Descriptive Statistics and Availability Of Processed Data Fo...mentioning

confidence: 99%

Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing

Kejriwal¹,

Nagaraj²

2023

Journal of Data Science

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“…Other studies show that in children's books, mothers play a dominant role in parenting, while fathers are underrepresented (Anderson et al, 2021). Some of these gender discrepancies disappear if the author is female (Nagaraj and Kejriwal, 2022). There has been growing interest in investigating gender bias in children's literature, specifically children's books, but there is little work on large collections of folk tales across multiple cultures.…”

Section: Gender Biasmentioning

confidence: 99%

Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales

Wu,

Wang,

Mihalcea

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Folk tales are strong cultural and social influences in children's lives, and they are known to teach morals and values. However, existing studies on folk tales are largely limited to European tales. In our study, we compile a large corpus of over 1,900 tales originating from 27 diverse cultures across six continents. Using a range of lexicons and correlation analyses, we examine how human values, morals, and gender biases are expressed in folk tales across cultures. We discover differences between cultures in prevalent values and morals, as well as cross-cultural trends in problematic gender biases. Furthermore, we find trends of reduced value expression when examining public-domain fiction stories, extrinsically validate our analyses against the multicultural Schwartz Survey of Cultural Values, and find traditional gender biases associated with values, morals, and agency. This largescale cross-cultural study of folk tales paves the way for future studies on how literature influences and reflects cultural norms.

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Dataset for studying gender disparity in English literary texts

Cited by 2 publications

References 1 publication

Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing

Quantifying Gender Disparity in Pre-Modern English Literature using Natural Language Processing

Cross-Cultural Analysis of Human Values, Morals, and Biases in Folk Tales

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