The effect of the typicality of song lyrics on song popularity: A natural language processing analysis of the British top singles chart
Bibliographic record
Abstract
This study investigates the relationship between the lexical typicality of song lyrics and song popularity in the UK Official Singles Chart from 1999 to 2013. Drawing on natural language processing (NLP) techniques, we develop a multidimensional measure of lyrics’ typicality that captures lexical repetition, complexity, thematic content, and emotional tone, addressing methodological limitations in previous research that relied heavily on static word dictionaries. By analysing 1,457 songs that reached the top five chart positions, we demonstrate that lyrics’ typicality significantly predicts the duration a song remains in the top five but does not affect the peak chart position or the trajectory of popularity (skewness and kurtosis). Our findings suggest that while typical lyrics may contribute to a song’s longevity within a given canon, they do not necessarily guarantee chart-topping success. This research contributes a replicable, dictionary-free methodology for assessing lyrics’ typicality and offers insights into the nuanced role of lyrics in shaping musical preferences.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".