Will I Remain Popular? A Study Case on Spotify
Online streaming platforms are now the most important form of music consumption. In this paper, we present a model for predicting if a popular song on Spotify will remain popular after a certain amount of time. Spotify is the second biggest global streaming service. If a song is popular on this plataform it will ensure a good financial return for the artist and his label. We approach the problem as a classification task and employ classificators built on past information from the plataform's Top 50 Global ranking. The Support Vector Machine with linear kernel classificator reached the best results. We also verify if acoustic information can provide useful features for this problem.We made a series of classication rounds, where the results of one round were used as input of posterior rounds. Our results show that rankings previous data alone is sufficient to predict if a song will remain at the Top 50 Global two months in advance, achieving accuracy, negative predictive value, recall, specificity and F1 Score higher than 70\% for this task.
Arakelyan, S., Morstatter, F., Martin, M., Ferrara, E., and Galstyan, A. (2018). Mining and forecasting career trajectories of music artists. In Proceedings of the 29th on Hypertext and Social Media, HT ’18, pages 11–19, New York, NY, USA. ACM.
Araújo, C. V., Neto, R. M., Nakamura, F. G., and Nakamura, E. F. (2017). Using complex networks to assess collaboration in rap music: A study case of dj khaled. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web, WebMedia ’17, pages 425–428, New York, NY, USA. ACM.
Brownlee, J. (2017). Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict the Future. Jason Brownlee.
Dhar, V. and Chang, E. A. (2009). Does chatter matter? the impact of user-generated content on music sales. Journal of Interactive Marketing, 23(4):300 – 307.
Dubnov, S. (2004). Generalization of spectral flatness measure for non-gaussian linear processes. IEEE Signal Processing Letters, 11(8):698–701.
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861 – 874. ROC Analysis in Pattern Recognition.
Harris, D. and Harris, S. (2010). Digital design and computer architecture. Morgan Kaufmann.
Herremans, D., Martens, D., and Sörensen, K. (2014). Dance hit song prediction. Journal of New Music Research, 43(3):291–302.
Interiano, M., Kazemi, K., Wang, L., Yang, J., Yu, Z., and Komarova, N. L. (2018). Musical trends and predictability of success in contemporary songs in and out of the top charts. Royal Society Open Science, 5(5):171274.
Lee, J. and Lee, J. (2018). Music popularity: Metrics, characteristics, and audio-based prediction. IEEE Transactions on Multimedia, 20(11):3173–3182.
McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, pages 18–25.
McKinney, W. (2010). Data structures for statistical computing in python. In van der Walt, S. and Millman, J., editors, Proceedings of the 9th Python in Science Conference, pages 51 – 56.
Olson, D. L. and Delen, D. (2008). Advanced data mining techniques. Springer Science & Business Media.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Shulman, B., Sharma, A., and Cosley, D. (2016). Predictability of popularity: Gaps between prediction and understanding. In Tenth International AAAI Conference on Web and Social Media, pages 348–357.
Silva, M. O., Rocha, L. M., and Moro, M. M. (2019). Collaboration profiles and their impact on musical success. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC ’19, pages 2070–2077, New York, NY, USA. ACM.
Steininger, D. M. and Gatzemeier, S. (2013). Using the wisdom of the crowd to predict popular music chart success. In Proceedings of the 21st European Conference on Information Systems, page 215.
Zhang, H. (2004). The optimality of naive bayes. AA, 1(2):3.