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      Large scale analysis of gender bias and sexism in song lyrics

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          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.

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          Author and article information

          Journal
          03 August 2022
          Article
          2208.02052
          583b88af-1a1f-4afb-bef0-dcdb6f956934

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          cs.CY

          Applied computer science
          Applied computer science

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