No abstract

The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era.

Este estudo tem como principal objetivo analisar como os dados do Twitter se relacionam com o sucesso da carreira artística musical. Primeiro, realizamos a coleta de dados de tweets, número de curtidas e retweets para cada perfil de artista presente nas paradas de sucesso do Spotify no mercado musical brasileiro. A partir dos dados coletados, construímos séries temporais para representar a carreira de cada artista, e investigamos se os períodos de maior sucesso ocorrem próximos uns dos outros. Tal análise exploratória auxilia na identificação de padrões temporais que podem revelar a existência de hot streaks, i.e., períodos de sucesso acima do normal. Então, analisamos quais são os termos mais frequentes antes e depois dos picos de sucesso de artistas.

The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era.