Abstract
Estimating and analyzing the popularity of an entity is an important task for professionals in several areas, e.g., music, social media, and cinema. Furthermore, the ample availability of online data should enhance our insights into the collective consumer behavior. However, effectively modeling popularity and integrating diverse data sources are very challenging problems with no consensus on the optimal approach to tackle them. To this end, we propose a non-linear method for popularity metric aggregation based on geometrical shapes derived from the individual metrics’ values, termed Geometric Aggregation of Popularity metrics (GAP). In this work, we particularly focus on the estimation of artist popularity by aggregating web-based artist popularity metrics. Finally, even though the most natural choice for metric aggregation would be a linear model, our approach leads to stronger rank correlation and non-linear correlation scores compared to linear aggregation schemes. More precisely, our approach outperforms the simple average method in five out of seven evaluation measures.
Highlights
Popularity is without a doubt an abstract notion that is used to express how much attention a certain item, person, or concept has received lately
We opted for Last.fm play counts and YouTube channel views as the ground truth for evaluation purposes. We chose these metrics because we believed that streaming activity reflected artist popularity more accurately than fan count, social media mentions, or proprietary “black-box” popularity scores (e.g., Spotify popularity)
We focused on artists that exhibited differences in their popularity among different popularity metrics, because otherwise, the aggregation methods would provide the same information as the individual metrics and the comparison among them would not yield noteworthy conclusions
Summary
Popularity is without a doubt an abstract notion that is used to express how much attention a certain item, person, or concept has received lately. The estimation of an entity’s popularity is desirable in many areas such as music [1], social media [2], science [3], cinema [4], and the Internet [5]. When multiple metrics concerning performance in general are available for each entity, an optimal approach for the aggregation of the metrics or the rankings is certainly of interest [7,8]. Many such methods have been proposed in the multi-criteria decision analysis (MCDA) research literature [9,10]. This study focuses on the estimation of music artist popularity. The traditional way to measure their popularity has been through sales and music top charts
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
