This is niche, but I’ve been wondering how well I can forecast the success my TikToks. Vertical blue lines are 80% CIs, horizontal blue line is median estimate, green dot is if the actual was within 10x of median, red x otherwise.
For the next year:
90% I have at least one video with 100k+ views
40% I have at least one video with 1M+ views
20% I make a serious effort to be popular again for at least a month
85% I have at least one video with 1M+ views conditional on me making a serious effort for at least a month
Thanks for sharing & good luck with the TikToks! I notice I’m curious about e.g. how likely 100M views on a video are, given 1M views (and similar questions).
Thanks! I previously found that my videos roughly fit a power-law distribution, and the one academic paper I could find on the subject also found that views were Zipf-distributed.
Since power law distributions are scale-invariant, I think it’s relatively easy to answer your question: p(100M|1M)∝p(100|1) etc. In that original post I thought that my personal views roughly fit the model P(Views≥10k)=0.3k−2; I haven’t looked at that recently though and expect the coefficients have changed.
This is niche, but I’ve been wondering how well I can forecast the success my TikToks. Vertical blue lines are 80% CIs, horizontal blue line is median estimate, green dot is if the actual was within 10x of median, red x otherwise.
For the next year:
90% I have at least one video with 100k+ views
40% I have at least one video with 1M+ views
20% I make a serious effort to be popular again for at least a month
85% I have at least one video with 1M+ views conditional on me making a serious effort for at least a month
Thanks for sharing & good luck with the TikToks! I notice I’m curious about e.g. how likely 100M views on a video are, given 1M views (and similar questions).
Disclaimer: Ben is my manager.
Thanks! I previously found that my videos roughly fit a power-law distribution, and the one academic paper I could find on the subject also found that views were Zipf-distributed.
Since power law distributions are scale-invariant, I think it’s relatively easy to answer your question: p(100M|1M)∝p(100|1) etc. In that original post I thought that my personal views roughly fit the model P(Views≥10k)=0.3k−2; I haven’t looked at that recently though and expect the coefficients have changed.