Decoding The Style And Bias of Song Lyrics

Barman, Manash Pratim; Awekar, Amit; Kothari, Sambhav

doi:10.1145/3331184.3331363

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…Language bias (RQ2): The second research question focuses on the differences of language bias in male and female solo artist song lyrics. Our results extend the ones obtained in [17], where all the WEAT scores are positive in magnitude. We enrich this work in two different ways.…”

Section: Discussionsupporting

confidence: 89%

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Large scale analysis of gender bias and sexism in song lyrics

Betti¹,

Abrate²,

Kaltenbrunner³

2022

Preprint

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We employ Natural Language Processing techniques to analyse 377,808 English song lyrics from the "Two Million Song Database" corpus, focusing on the expression of sexism across five decades 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.

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Section: Discussionsupporting

confidence: 89%

“…Here we are more interested in studies using large datasets like the one from [17], which analyses bias in half a million song lyrics using WEAT scores [10]. This study does not segregate its results neither by gender nor in the temporal dimension and finds that bias in songs is strongest in relation to gender stereotypes and career paths.…”

Section: Related Workmentioning

confidence: 99%

Large scale analysis of gender bias and sexism in song lyrics

Betti¹,

Abrate²,

Kaltenbrunner³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Here we are more interested in studies using large datasets like the one from [34], which analyses bias in half a million song lyrics using WEAT scores [11]. This study does not segregate its results neither by gender nor in the temporal dimension and finds that bias in songs is strongest in relation to gender stereotypes and career paths.…”

Section: Related Workmentioning

confidence: 99%

Large scale analysis of gender bias and sexism in song lyrics

2023

View full text Add to dashboard Cite

We employ Natural Language Processing techniques to analyse 377,808 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.

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“…Other highprofile papers such as Garg et al (2018) and Lewis and Lupyan (2020) have used the WEAT to study cultural biases across time and place. Importantly, the method is now being used to evaluate the political biases of websites (Knoche et al 2019), detect the purposeful spread of misinformation on social media by state-sponsored actors (Toney et al 2021), uncover biases present in and proliferated through popular song lyrics (Barman, Awekar, and Kothari 2019), and even to measure how much gender bias US judges display in their judicial opinions (Ash, Chen, and Galletta 2021).…”

Section: Related Workmentioning

confidence: 99%

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

Loon

Giorgi

Willer

et al. 2022

ICWSM

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The word embedding association test (WEAT) is an important method for measuring linguistic biases against social groups such as ethnic minorities in large text corpora. It does so by comparing the semantic relatedness of words prototypical of the groups (e.g., names unique to those groups) and attribute words (e.g., ‘pleasant’ and ‘unpleasant’ words). We show that anti-Black WEAT estimates from geo-tagged social media data at the level of metropolitan statistical areas strongly correlate with several measures of racial animus—even when controlling for sociodemographic covariates. However, we also show that every one of these correlations is explained by a third variable: the frequency of Black names in the underlying corpora relative to White names. This occurs because word embeddings tend to group positive (negative) words and frequent (rare) words together in the estimated semantic space. As the frequency of Black names on social media is strongly correlated with Black Americans’ prevalence in the population, this results in spuriously high anti-Black WEAT estimates wherever few Black Americans live. This suggests that research using the WEAT to measure bias should consider term frequency, and also demonstrates the potential consequences of using black-box models like word embeddings to study human cognition and behavior.

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Decoding The Style And Bias of Song Lyrics

Cited by 7 publications

References 11 publications

Large scale analysis of gender bias and sexism in song lyrics

Large scale analysis of gender bias and sexism in song lyrics

Large scale analysis of gender bias and sexism in song lyrics

Negative Associations in Word Embeddings Predict Anti-black Bias across Regions–but Only via Name Frequency

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