WHO IS »YOU'RE SO VAIN« ABOUT? REFERENCE IN POPULAR MUSIC LYRICS

2018 ◽  
pp. 245-262
Keyword(s):  
Popular Music ◽  
Music Lyrics

scholarly journals Themes of Lust and Love in Popular Music Lyrics From 1971 to 2011

SAGE Open ◽  
2014 ◽  
Vol 4 (3) ◽  
pp. 215824401454717 ◽  
Author(s):  
Yasaman Madanikia ◽  
Kim Bartholomew
Keyword(s):  
Popular Music ◽  
Music Lyrics

scholarly journals Popular music lyrics and musicians’ gender over time: A computational approach

2019 ◽  
pp. 030573561987160 ◽  
Author(s):  
Manuel Anglada-Tort ◽  
Amanda E Krause ◽  
Adrian C North
Keyword(s):  
Machine Learning ◽  
Popular Music ◽  
Machine Learning Techniques ◽  
Mixed Effect ◽  
Gender Distribution ◽  
Learning Techniques ◽  
Inflection Points ◽  
The Uk ◽  
Music Lyrics ◽  
Over Time

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.

“Where is the love?” Topics and prosocial behavior in German popular music lyrics from 1954 to 2014

2018 ◽  
Vol 23 (4) ◽  
pp. 508-524 ◽  
Author(s):  
Nicolas Ruth
Keyword(s):  
Content Analysis ◽  
Popular Music ◽  
Prosocial Behavior ◽  
Social Engagement ◽  
Prosocial Behaviors ◽  
Equal Rights ◽  
Quantitative Content ◽  
Major Interest ◽  
Content Analyses ◽  
Music Lyrics

Many content analyses have investigated the content of popular music, but as yet no one has looked for references to prosocial behavior in the lyrics. There are no quantitative content analyses of prosocial content in popular music, although we know that many musicians are concerned with social engagement, the environment, equal rights, and many other prosocial behaviors. To investigate which topics are the most prevalent in popular music lyrics and how frequently these refer to prosocial behavior, a content analysis was performed on 588 songs appearing in the German yearly charts from 1954 to 2014. The major interest of songwriters seems to be love, which was found in 57% of the songs; this was the most common topic found. References to prosocial behavior were found in 3.74% of the songs. Prosocial behavior usually appeared in songs dealing with social or political topics.

scholarly journals Popular music lyrics and the COVID-19 pandemic

2021 ◽  
pp. 030573562110451
Author(s):  
Kaila C Putter ◽  
Amanda E Krause ◽  
Adrian C North
Keyword(s):  
United States ◽  
United Kingdom ◽  
Popular Music ◽  
Positive Association ◽  
Emotional Valence ◽  
The United States ◽  
Emotional Content ◽  
Human Interest ◽  
The United Kingdom ◽  
Music Lyrics

A limited amount of previous research suggests that deteriorating socioeconomic conditions may be associated with greater popularity of music lyrics featuring negative emotional content and references to relationships. The present research considered this in charting popular music before and during the first 6 months of the COVID-19 pandemic. A dataset based on the song lyrics of the top-5 charting weekly songs in the United Kingdom and the United States from January 1999 to August 2020 was computer-analyzed for interpersonal variables, such as satisfaction and human interest, and positive and negative emotional valence. Results indicated lower satisfaction and human interest in lyrics in the United States and United Kingdom in the first 6 months of the COVID-19 pandemic compared to the lyrics in charting songs in 2015–2019. The US charting songs in 2020 also saw higher leveling and negative emotional content, and, when considering monthly data from 1999 to 2020, there was a positive association between economic misery and the number of negatively valenced words. The findings broaden our understanding of the relationship between significant global events and trends in popular music.

scholarly journals From “I Want To Hold Your Hand” to “Promiscuous”: Sexual Stereotypes in Popular Music Lyrics, 1960–2008

2017 ◽  
Vol 21 (4) ◽  
pp. 1083-1105 ◽  
Author(s):  
Andrew P. Smiler ◽  
Jennifer W. Shewmaker ◽  
Brittany Hearon
Keyword(s):  
Popular Music ◽  
Sexual Stereotypes ◽  
Music Lyrics

Objectification in Popular Music Lyrics: An Examination of Gender and Genre Differences

Sex Roles ◽  
2016 ◽  
Vol 75 (3-4) ◽  
pp. 164-176 ◽  
Author(s):  
Mark A. Flynn ◽  
Clay M. Craig ◽  
Christina N. Anderson ◽  
Kyle J. Holody
Keyword(s):  
Popular Music ◽  
Gender And Genre ◽  
Music Lyrics

scholarly journals Sounds like meritocracy to my ears: Exploring the link between inequality in popular music and private culture

2021 ◽  
Author(s):  
Luca Carbone ◽  
Jonathan Jan Benjamin Mijs
Keyword(s):  
Popular Music ◽  
Economic Inequality ◽  
Public Culture ◽  
Future Research ◽  
Popular Songs ◽  
Original Dataset ◽  
Automatic Text Classification ◽  
User Data ◽  
Music Lyrics ◽  
The Impact

Economic inequality is on the rise in Western societies and ‘meritocracy’ remains a widespread narrative used to justify it. An emerging literature has documented the impact of meritocratic narratives in media, mostly focusing on newspapers. In this paper, we study music as a potential source of cultural frames about economic inequality. We construct an original dataset combining user data from Spotify with lyrics from Genius to inductively explore whether popular music features themes of economic inequality. In order to do so, we employ unsupervised computational text analysis to classify the content of the 3,660 most popular songs across 23 European countries. Informed by Lizardo’s enculturation framework, we study popular music lyrics through the lens of public culture and explore its links with individual beliefs about inequality as a reflection of private culture. We find that, in more unequal societies, songs that frame inequalities as a structural issue (songs about “Struggle” or omnipresent “Risks”) are more popular than those adopting a meritocratic frame (songs we describe as “Bragging Rights” or those telling a “Rags to Riches” tale). Moreover, we find that the presence in public culture of a certain frame is associated with the expression of frame-consistent individual beliefs about inequality (private culture). We conclude by offering reflections on the promise of automatic text classification for the study of music lyrics, the theorized role of popular music in the study of culture, and by proposing venues for future research.

scholarly journals Identifying Narrative Contexts in Brazilian Popular Music Lyrics Using Sparse Topic Models: A Comparison Between Human-Based and Machine-Based Classification

2019 ◽  
Author(s):  
André Dalmora ◽  
Tiago Tavares
Keyword(s):  
Machine Learning ◽  
Popular Music ◽  
Life Stories ◽  
Great Part ◽  
Topic Models ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Part Of Speech ◽  
Popular Songs ◽  
Music Lyrics

Music lyrics can convey a great part of the meaning in popular songs. Such meaning is important for humans to understand songs as related to typical narratives, such as romantic interests or life stories. This understanding is part of affective aspects that can be used to choose songs to play in particular situations. This paper analyzes the effectiveness of using text mining tools to classify lyrics according to their narrative contexts. For such, we used a vote-based dataset and several machine learning algorithms. Also, we compared the classification results to that of a typical human. Last, we compare the problems of identifying narrative contexts and of identifying lyric valence. Our results indicate that narrative contexts can be identified more consistently than valence. Also, we show that human-based classification typically do not reach a high accuracy, which suggests an upper bound for automatic classification. narrative contexts. For such, we built a dataset containing Brazilian popular music lyrics which were raters voted online according to its context and valence. We approached the problem using a machine learning pipeline in which lyrics are projected into a vector space and then classified using general-purpose algorithms. We experimented with document representations based on sparse topic models [11, 12, 13, 14], which aims to find groups of words that typically appear together in the dataset. Also, we extracted part-of-speech tags for each lyric and used their histogram as features in the classification process.

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