Google executives acknowledged this month they need to do a better job surfacing user-generated content after the recent Reddit blackouts.
Google executives acknowledged this month they need to do a better job surfacing user-generated content after the recent Reddit blackouts.
undefined> I have to go watch 10 YouTube videos to get an idea, and even some of THOSE are text to speech product spec regurgitators, again just content farming for affiliate links.
Not to mention the removal of dislikes on Youtube, which makes it even HARDER to find quality tutorial type videos
there’s a browser plug in for that.
Which isn’t entirely accurate if at all. It extrapolates the dislikes from its own database ie users who have it installed. Compared to the entire user base of Youtube this is an incredibly tiny sample size.
You need a much, much smaller sample size than you think. Estimates for Youtube’s monthly unique visits range from ~2 billion to about ~2.7 billion. For a 5% margin of error at a 99.9% confidence level, you’d only need to sample 1083 people to get an accurate sample size.
I’m positive that extension has more than 1000 users.
Don’t you also need to worry about your sample population being biased? You’d only be sampling people who sought out a dislike plugin, these people might be much more likely to dislike a video. Is there any way to account for that?
You’d have to have a separate cohort of non-plugin users & another with a sampling of both, I think. Run some regressions on those data and I think you’d be able to tease out any bias that exists.