Credit to @lilweehag for help with the scraper.
I’ve created a representative sample of ~20,000 TikTok videos. I’ve only found one paper analyzing the view counts of videos, which claims that the most popular videos were Zipf-distributed. That may be true of the most popular videos, but at least in my sample they are not perfectly power law distributed:
Regardless of the exact distribution, it’s easy to see that the distribution is fairly fat tailed, so I was curious what the Gini coefficient and Lorenz curves looks like.
Here’s the Lorenz curve for view counts in 2020:
The black line indicates perfect equality. The green x’s indicate actual data.
As you can see, it is almost perfectly unequal. Indeed, the Gini coefficient is around 0.93. (0 indicates perfect equality, 1 indicates perfect inequality.)
To put that into perspective, here’s the Gini coefficients of some other things:
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Summary : There is a widespread belief that the first video one creates on TikTok 1) gets an automatic “boost” from the recommendation algorithm and 2) has extra importance in influencing future video success. I test these claims on a data set of 33,925 videos and find that both propositions are unsupported by the evidence. If you have been holding off on creating your first video because you want it to be perfect, you should just go ahead and make an imperfect one. Background I sometimes hear people argue that the first video one creates on TikTok is unusually important. For example : The first video that any user on Tik Tok posts gets an instant spike in impressions. As a new user, Tik Tok metaphorically “throws you a bone” and will in most cases show your video to more people right off the bat than it would a video by a repeat poster. This means that it’s vital to make your very first post on Tik Tok engaging. Unlike Youtube, for example, where you can feel free to po...
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Summary TikTok’s recommendation algorithm is an iterative process which repeatedly displays videos to larger and larger test audiences, until engagement from the test audience falls below some threshold. It is unknown how “engagement” is measured, but it is usually speculated to be some combination of likes, comments, and shares. Various people have hypothesized the weights of these metrics (e.g. one share = 10 likes), but, to our knowledge, no one has ever measured it. We therefore attempted to measure this on a representative sample of ~20,000 videos. Our research concludes that these metrics are highly collinear and no predictive value is found from comment and share count. (I.e. you should just optimize for likes, and don’t need to optimize for shares or comments .) You will need about 1/5 of your viewers to like your video in order for it to be promoted . Background and Motivation More background on the structure of the TikTok algorithm can be found here . Veed cont...
Are you sure your sample is representitive?
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Awesome post! Do you know how this Gini coefficient compares to YouTube and Twitter?
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