The economics of social media (super-)stars: an empirical investigation of stardom and success on YouTube

Abstract

A new type of superstars developed with the rise of social media markets: social media superstars (SMS). The economic literature on the superstar phenomenon provides empirical evidence on different types of stars, above all athletes and musicians. This paper presents the first empirical work on SMS with original data from YouTube. Using various econometric techniques, we analyze a unique sample of 200 YouTube stars out of four different video categories to shed light on this new phenomenon. Beyond applying classic economic concepts on superstars by Rosen, MacDonald, and Adler, we contribute to extending the literature toward attention economics and the concepts of audience building (audience attraction and maintenance). In our empirical analysis, we find evidence supporting an inverse U-shape influence of upload frequency on the success of SMS. Moreover, we find empirical evidence that the duration and experience in the market have a significant influence on social media success. Eventually, we derive implications for superstar theory, SMS management, service providers, and advertisers.

The battle of YouTube, TV and Netflix: an empirical analysis of competition in audiovisual media markets

“…), i.e., a sort of user-exchange of contents, and professional contents from business companies are in the clear minority. The nature of YouTube's early 'user generated content' (from users for users) has changed a lot and initial 'private' uploaders professionalized towards being active content providers, offering regular video uploads regarding specific topics according to the channel's media concept (Döring 2014;Budzinski and Gaenssle 2020). Notwithstanding the still existing type of non-professional content, this trend of professionalization points towards the significant turnovers and revenues that content providers such as so-called social media stars 4 earn through participation on YouTube's advertisement revenues as well as through product placements-with the latter further emphasizing the commercial nature of the content supply (Budzinski and Gaenssle 2020;Gaenssle and Budzinski 2021).…”

“…The nature of YouTube's early 'user generated content' (from users for users) has changed a lot and initial 'private' uploaders professionalized towards being active content providers, offering regular video uploads regarding specific topics according to the channel's media concept (Döring 2014;Budzinski and Gaenssle 2020). Notwithstanding the still existing type of non-professional content, this trend of professionalization points towards the significant turnovers and revenues that content providers such as so-called social media stars 4 earn through participation on YouTube's advertisement revenues as well as through product placements-with the latter further emphasizing the commercial nature of the content supply (Budzinski and Gaenssle 2020;Gaenssle and Budzinski 2021). Nowadays, a significant share, if not most of the views on YouTube, fall on commercial content, most of which is professionally produced; the most popular 20% receive 97% of views (Ding et al 2011) and 10-30% of videos have fewer than ten views (Chowdhury & Makaroff 2013).…”

Quantifying Voter Biases in Online Platforms

“…Reputation Correlation [7], [45], [55], Show some evidence of correlation between past reputation and current Bias Study [46], [37], [8] success [7,45,55,46,37,8].…”

“…Reputation Bias. Prior works on online reputation suggest that past reputation may be useful in predicting current success [7,45,55,46,37,8] (also known as "superstar economics" [39]). Beuscart et al [7] observed that in MySpace Music, most of the audience is focused on a few stars.…”

“…Liang [37] showed that in Reddit, users with higher comment karma tend to produce questions and comments with higher ratings. Budzinski et al [8] analyzed a sample of YouTube stars to show that past success positively and significantly influences current success. While these prior studies show the evidence of reputation bias, they do not provide any bias quantification.…”