Many different factors have been studied in connection with songs’ top chart dynamics, including popularity (‘superstar’) status and the demographics of the artist(s), the level of promotion, the label/company, etc these factors could be roughly classified as ‘socioeconomic’ factors (as opposed to ‘musical’ factors). They found that geographical and cultural distances played a different role in songs’ success in different eras (analogue, digital and Internet). Buda & Jarynowski investigated trends in songs’ popularity in 12 western European countries by analysing data from weekly charts between 19. It was found that, since the advent of file-sharing networks and related market changes, the probability of songs’ survival in the charts decreased, and the albums that do survive then have much improved prospects. developed a stochastic model of the distribution of album longevity in the Billboard Top 100 Chart. One of the earlier studies focused on the relationship between the song’s position in the charts and the duration of time that the song stayed in the charts, and found that greatest hits (that is, songs that scored highest) remained in the charts for longer periods. Different correlates of success have been identified and studied. In this exploratory study, we focus on the popular songs of the last three decades, and attempt to extract multi-decadal trends in the musical attributes of both successful and less successful songs, and to see what it is that makes songs popular in different years.Ī number of authors have investigated the popularity of songs and analysed the dynamics of top charts. In particular, it has been noticed that different generations like different types of music. There is something magical about music and scientists have been trying to disentangle the magic and explain what it is that makes us love some music, hate other music and just listen to music. Music, and, in particular, songs, rarely leave people unmoved. This allowed quantification of the contribution of purely musical characteristics in the songs’ success, and suggested the time scale of fashion dynamics in popular music. We used random forests to predict the success of songs, first based on their acoustic features, and then adding the ‘superstar’ variable (informing us whether the song’s artist had appeared in the top charts in the near past). In the context of some features, successful songs pre-empt the dynamics of all songs, and in others they tend to reflect the past. The difference between successful and average songs is not straightforward. In particular, they tend to be ‘happier’, more ‘party-like’, less ‘relaxed’ and more ‘female’ than most.

Interestingly, successful songs exhibit their own distinct dynamics. Furthermore, songs are becoming less ‘male’. For example, there is a clear downward trend in ‘happiness’ and ‘brightness’, as well as a slight upward trend in ‘sadness’. Several multi-decadal trends have been uncovered. We analyse more than 500 000 songs released in the UK between 19 to understand the dynamics of success (defined as ‘making it’ into the top charts), correlate success with acoustic features and explore the predictability of success.

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