Popular music lyrics and musicians’ gender over time: a computational approach

Krause, Amanda E, Anglada-Tort, Manuel, and North, Adrian C (2019) Popular music lyrics and musicians’ gender over time: a computational approach. Psychology of Music. (In Press)

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Abstract

The present study investigated how the gender distribution of the United Kingdom’s most popular artists has changed over time and the extent to which these changes might relate to popular music lyrics. Using data mining and machine learning techniques, we analyzed all songs that reached the UK weekly top 5 sales charts from 1960 to 2015 (4,222 songs). DICTION software facilitated a computerized analysis of the lyrics, measuring a total of 36 lyrical variables per song. Results showed a significant inequality in gender representation on the charts. However, the presence of female musicians increased significantly over the time span. The most critical inflection points leading to changes in the prevalence of female musicians were in 1968, 1976, and 1984. Linear mixed-effect models showed that the total number of words and the use of self-reference in popular music lyrics changed significantly as a function of musicians’ gender distribution over time, and particularly around the three critical inflection points identified. Irrespective of gender, there was a significant trend toward increasing repetition in the lyrics over time. Results are discussed in terms of the potential advantages of using machine learning techniques to study naturalistic singles sales charts data.

Item ID: 62718
Item Type: Article (Research - C1)
ISSN: 1741-3087
Keywords: popular music, lyrics, gender, DICTION, sales charts, machine learning
Copyright Information: Under SAGE's Green Open Access policy, the Accepted © The © Author(s) 2019. The Accepted Version is restricted to non-commercial and no derivative uses.
Date Deposited: 28 Jul 2020 04:25
FoR Codes: 19 STUDIES IN CREATIVE ARTS AND WRITING > 1904 Performing Arts and Creative Writing > 190499 Performing Arts and Creative Writing not elsewhere classified @ 40%
17 PSYCHOLOGY AND COGNITIVE SCIENCES > 1701 Psychology > 170113 Social and Community Psychology @ 60%
SEO Codes: 95 CULTURAL UNDERSTANDING > 9501 Arts and Leisure > 950101 Music @ 40%
95 CULTURAL UNDERSTANDING > 9502 Communication > 950204 The Media @ 30%
97 EXPANDING KNOWLEDGE > 970117 Expanding Knowledge in Psychology and Cognitive Sciences @ 30%
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