Language models for longitudinal analysis of abusive content in Billboard Music Charts
This addresses the lack of validated trends in harmful content for policy development, particularly affecting children and youths, but is incremental as it applies existing methods to new data.
The study tackled the problem of increasing abusive and sexually explicit content in Billboard Music Charts music by analyzing lyrics from the last seven decades using deep learning and language models, finding a significant rise in explicit content from 1990 onwards with increasing prevalence of profane and inappropriate language.
There is no doubt that there has been a drastic increase in abusive and sexually explicit content in music, particularly in Billboard Music Charts. However, there is a lack of studies that validate the trend for effective policy development, as such content has harmful behavioural changes in children and youths. In this study, we utilise deep learning methods to analyse songs (lyrics) from Billboard Charts of the United States in the last seven decades. We provide a longitudinal study using deep learning and language models and review the evolution of content using sentiment analysis and abuse detection, including sexually explicit content. Our results show a significant rise in explicit content in popular music from 1990 onwards. Furthermore, we find an increasing prevalence of songs with lyrics containing profane, sexually explicit, and otherwise inappropriate language. The longitudinal analysis of the ability of language models to capture nuanced patterns in lyrical content, reflecting shifts in societal norms and language use over time.