Collaboration, cognitive effort, and self-reference in United Kingdom top 5 pop music lyrics 1960–2015.

Abstract

This research investigated associations between the lyrics of every song to have reached the weekly United Kingdom top 5 singles chart from 1960-2015 and the number of people responsible for recording each song. Following computerised content analysis of the lyrics of the 4,534 unique songs, the results showed that the number of musicians involved was related negatively to use of cognitive terms, consistent with previous research on social loafing; and was also related negatively to instances of self-reference and use of language concerning social interaction, arguably in reflection of the inherent constraints on such that arise from collaborating with others.

Popular music lyrics and the COVID-19 pandemic

“…As per Krause and North (2020), song lyrics were largely obtained using web-based sources (e.g., www.azlyrics.com). Where a song had both an original and a remixed version on separate country's charts, each version was treated as a separate song and both were included in the analyses.…”

“…The remaining 185 unique songs appeared on either the United States (n = 63) or United Kingdom (n = 122) chart. Krause and North (2020), sentiment analysis was conducted for the lyrics of each song using DICTION 7.0 content analysis software. This contains 10,000 words classified into a total of 35 discrete variables (Hart et al, 2013).…”

“…As outlined in Krause and North (2020), sentiment analysis was conducted for the lyrics of each song using Diction 7.0 content analysis software. This contains 10,000 words LYRICS AND COVID, 12 classified into a total of 35 discrete variables (Hart et al, 2013).…”

“And you don't like, don't like the way I talk”: Authenticity in the language of bruce springsteen.

“…Focusing on a single male artist is another important limitation, particularly given that gender differences have been reported in lyrical composition (Krause & North, 2019), including language characteristics related to our study such as self-references and the total number of words per song (Anglada-Tort et al, 2019). In addition, song lyrics vary depending on the number of people involved in musical composition (Krause & North, 2020), suggesting that the findings from singer-songwriters like Springsteen may not generalize to music written in dyads or groups. Finally, the observed results may be interpreted within the context of cultural and historical realities occurring at the time albums are released.…”

Mainstream popular music research: a musical update

“…By and large, also in the listener-and listening-function-focussed area of mainstream popular music analysis, one can observe a trend towards the use of quantitative (computerised) measurement instruments. 24 However, recent studies concerned with a vast amount of songs from the UK or US charts in connection with notions of emotion and mood, for example, North et al (2018;2019a, b) and Krause and North (2020), skip the step of asking listeners directly for their experiences in favour of artificial intelligence processing. Some other quantitative studies utilising machine learning in the field of music emotion recognition integrate popular music listeners' perspectives to some extent in that they combine audio feature extraction with the retrieval of social, tag-based emotion annotations from services such as Last.fm (Song et al 2012;Jamdar et al 2015;Aljanaki et al 2017).…”