Trends in Positive, Negative, and Neutral Themes of Popular Music From 1998 to 2018: Observational Study
Bibliographic record
Abstract
BACKGROUND: Across the United States, the incidence of adolescent depression and suicide cases has risen in the past 10 years. Despite the risk factors and causes being multifactorial, the influence of popular culture on society and adolescents in this media-driven generation cannot be mitigated. Although the impact of social media and its effect on shaping self-identity in adolescents have been observed, the impact of music and its potential for subliminal negative messages to adolescents remains unclear. OBJECTIVE: This study analyzes the lyrics and music videos of the most popular music of multiple genres to quantify the frequencies of varying music theme trends. METHODS: The frequencies of themes of 1052 total American and Latin songs were collected from the Nielsen Music and Billboard's top 100 chart performance from 1998 to 2018 for hip hop/rhythm and blues (R&B), pop, Latin, country, and rock/metal genres. Themes from songs were identified, quantified, and categorized with a rubric into negative, neutral, and positive themes by 3 different reviewers. Analysis was performed using 2-tailed t tests and a generalized linear model. RESULTS: Popular songs were reviewed for positive, negative, and neutral themes in the following 3-year intervals for ease of analysis purposes: 1998 to 2000 (n=148), 2001 to 2003 (n=150), 2004 to 2006 (n=148), 2007 to 2009 (n=156), 2010 to 2012 (n= 150), 2013 to 2015 (n=150), and 2016 to 2018 (n=150). There was a significant 180% increase in the percentage of songs with negative themes between all the interval years and across all genres (P<.001), while there was no significant difference in the frequency of songs with positive (P=.54) or neutral (P=.26) themes by year. There were significant differences in the number of negative themes found across genres (P<.001), with hip hop/R&B having the highest frequency of 130 out of 208 (62.5%) of the negative themes when compared to each of the individual genres (P<.001). CONCLUSIONS: This study shows there is an increase in the frequency of negative themes over the span of 20 years across all genres, with hip hop/R&B having the highest frequency among the genres. These findings point to the potential impact that music may have in popular culture and on society. Furthermore, these results can help shape discussions between caregivers and their adolescent dependents and between primary care providers and their adolescent patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".