Can we predict the Billboard music chart winner? Machine learning prediction based on Twitter artist-fan interactions

ABSTRACT

The Billboard chart is a clear barometer for measuring a song's success in the music industry. Therefore, a number of artists and affiliated marketers in the music industry have attempted to determine how to emerge at the top of the chart. In the current study, artist-fan interactions on social media are examined as one of the possible indicators to predict the success of songs on the Billboard Hot 100 chart. The performance of a song on the Billboard chart was predicted based on the artist-fan interaction using the artist-fan dataset composed of posts, comments, and quote tweets, their sentimental levels, and the interaction styles of each post. Overall, the XGBoost model with the quote-tweet interaction data exhibited the highest classification performance (F1-score: 80.75% on Top 1 label), showing that the interaction features extracted from quote-tweets show the strongest relevance to a song's success. We present a simplified approach for observing and understanding public perception for the entertainment industry, specifically for the music industry, through social media interactions. We also suggest the facilitation of artist-fan interactions on social media with similar functions of quote-tweet function on Twitter as a valid strategy to make songs more successful.

KEYWORDS:

• Billboard
• Twitter
• interaction
• machine learning
• music industry

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.billboard.com/articles/news/467859/about-us.

2 https://twitter.com.

3 https://github.com/cjhutto/vaderSentiment.

4 https://scikit-learn.org/stable/index.html.

5 https://www.tensorflow.org.

Additional information

Funding

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICAN (ICT Challenge and Advanced Network of HRD) program (2020-0-01816) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1C1C1004324).