# Language models for longitudinal analysis of abusive content in Billboard Music Charts

arXiv: https://arxiv.org/abs/2510.06266 · HTML: https://mlsanity.com/paper/2510.06266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 60 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic](https://mlsanity.com/paper/2006.10842/) (2020)
- [Transformer-based approach towards music emotion recognition from lyrics](https://mlsanity.com/paper/2101.02051/) (2021)
- [Large language models for newspaper sentiment analysis during COVID-19: The Guardian](https://mlsanity.com/paper/2405.13056/) (2024)
- [Love Me, Love Me, Say (and Write!) that You Love Me: Enriching the WASABI Song Corpus with Lyrics Annotations](https://mlsanity.com/paper/1912.02477/) (2019)

## Built on by (most cited first, 1 of 1)

- [Towards an LLM-based method for quantifying the sexual content in song lyrics](https://mlsanity.com/paper/2608.08885/) (2026)

## Abstract (arXiv)

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.