Are users attracted by playlist titles and covers? Understanding playlist selection behavior on a music streaming platform

Abstract

Playlists have become the main entry point for users to obtain music resources. This study aimed to investigate the features of playlist titles and covers that attract users and the consequences of playlist selection on music streaming platforms. In this study, 7,606 playlist data were collected from the NetEase Cloud Music platform. The linguistic style of playlist titles was classified based on the language expectancy theory (LET). Artificial intelligence technology was used to recognize and classify the image style of playlist covers according to the content of image features. We then employed hierarchical multiple linear regression and grouping regression to study the moderating effects of playlist features (linguistic style of titles and image style of covers) on the relationship between the number of playlist comments and the number of plays. The findings revealed that (1) the number of playlist comments (comment-count) has a stronger positive relationship with the number of plays (play-count) when the linguistic style of titles, such as concrete, perceptual, interactive, and social languages, appears more frequently and (2) the image style of playlist covers, such as natural, non-natural, painting and text, and portrait images, strengthens the relationships between playlist comment-count and linguistic style of playlist titles on play-count.

Keywords

• User-generated playlist (UGP)
• Playlist selection
• Language expectancy theory (LET)
• Dual coding theory
• Music streaming platform