Large scale analysis of gender bias and sexism in song lyrics

• URL: http://arxiv.org/abs/2208.02052v5
• Date: Tue, 2 May 2023 16:52:30 GMT
• Title: Large scale analysis of gender bias and sexism in song lyrics
• Authors: Lorenzo Betti, Carlo Abrate, Andreas Kaltenbrunner
• Abstract summary: We identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.
• Score: 3.437656066916039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We employ Natural Language Processing techniques to analyse 377808 English song lyrics from the "Two Million Song Database" corpus, focusing on the expression of sexism across five decades (1960-2010) and the measurement of gender biases. Using a sexism classifier, we identify sexist lyrics at a larger scale than previous studies using small samples of manually annotated popular songs. Furthermore, we reveal gender biases by measuring associations in word embeddings learned on song lyrics. We find sexist content to increase across time, especially from male artists and for popular songs appearing in Billboard charts. Songs are also shown to contain different language biases depending on the gender of the performer, with male solo artist songs containing more and stronger biases. This is the first large scale analysis of this type, giving insights into language usage in such an influential part of popular culture.

Related papers

• Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias Measurements [1.5379084885764847]
  This paper uses topic modeling and bias measurement techniques to analyze and determine gender bias in English song lyrics. We observe large amounts of profanity and misogynistic lyrics on various topics, especially in the overall biggest cluster. We find that words related to intelligence and strength tend to show a male bias across genres, as opposed to appearance and weakness words, which are more female-biased.
  arXiv  Detail & Related papers  (2024-09-24T10:24:53Z)
• Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
  Existing machine translation gender bias evaluations are primarily focused on male and female genders. This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words) We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
  arXiv  Detail & Related papers  (2024-07-23T08:13:51Z)
• Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels [3.0623865942628594]
  We propose a framework for analyzing gender bias in terms of female objectification. Our framework measures female objectification along two axes. Applying our framework to 19th and 20th century novels reveals evidence of female objectification.
  arXiv  Detail & Related papers  (2024-03-25T20:16:14Z)
• Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts [87.62403265382734]
  Recent studies show that traditional fairytales are rife with harmful gender biases. This work aims to assess learned biases of language models by evaluating their robustness against gender perturbations.
  arXiv  Detail & Related papers  (2023-10-16T22:25:09Z)
• VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution [80.57383975987676]
  VisoGender is a novel dataset for benchmarking gender bias in vision-language models. We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas. We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
  arXiv  Detail & Related papers  (2023-06-21T17:59:51Z)
• Identifying gender bias in blockbuster movies through the lens of machine learning [0.5023676240063351]
  We gathered scripts of films from different genres and derived sentiments and emotions using natural language processing techniques. We found specific patterns in male and female characters' personality traits in movies that align with societal stereotypes. We used mathematical and machine learning techniques and found some biases wherein men are shown to be more dominant and envious than women.
  arXiv  Detail & Related papers  (2022-11-21T09:41:53Z)
• Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics [3.4048739113355215]
  We provide a comprehensive analysis of group-based biases in widely-used static English word embeddings trained on internet corpora. Using the Single-Category Word Embedding Association Test, we demonstrate the widespread prevalence of gender biases. We find that, of the 1,000 most frequent words in the vocabulary, 77% are more associated with men than women.
  arXiv  Detail & Related papers  (2022-06-07T15:35:10Z)
• Quantifying Gender Bias in Consumer Culture [0.0]
  Song lyrics may help drive shifts in societal stereotypes towards women, and that lyrical shifts are driven by male artists. Natural language processing of a quarter of a million songs over 50 years quantifies misogyny. Women are less likely to be associated with desirable traits (i.e., competence) and while this bias has decreased, it persists.
  arXiv  Detail & Related papers  (2022-01-10T05:44:54Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)
• Gender bias in magazines oriented to men and women: a computational approach [58.720142291102135]
  We compare the content of a women-oriented magazine with that of a men-oriented one, both produced by the same editorial group over a decade. With Topic Modelling techniques we identify the main themes discussed in the magazines and quantify how much the presence of these topics differs between magazines over time. Our results show that the frequency of appearance of the topics Family, Business and Women as sex objects, present an initial bias that tends to disappear over time.
  arXiv  Detail & Related papers  (2020-11-24T14:02:49Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.