The author notes that they used "cheats". Depending on what these do the iid assumption of the samples being independent could be violated. If it is akin to snowball sampling it could have an "excessive" success rate thereby inflating the numbers.
> Jason found a couple of cheats that makes the method roughly 32,000 times as efficient, meaning our “phone call” connects lots more often
> it was discovered by Jia Zhou et. al. in 2011, and it’s far more efficient than our naïve method. (You generate a five character string where one character is a dash – YouTube will autocomplete those URLs and spit out a matching video if one exists.)
I assume the cheat is something like using the playlist API that returns individual results for whether a video exists or not.
So you issue an API to create a playlist with video IDs x, x+1, x+2, ..., and then when you retrieve the list, only x+2 is in it since it is the assigned ID.
The data probably wouldn't look so clean if it were skewed. If Google were doing something interesting it probably wouldn't be skewed only by a little bit.
Admittedly, I did not read the paper linked. But my point is not about google doing something funny. Even if we assume that ids are truly random and uniformly distributed this does not mean that the sampling method doesn't have to be iid. This problem is similar to density estimation where Rejection sampling is super inefficient but converges to the correct solution, but MCMC type approaches might need to run multiple times to be sure to have found the solution.
Proving that using cheats and auto complete does not break sample independence and keeps sampling as random as possible would be needed here for stats beginners such as me!
Drunk dialing but having a human operator that each time tries to help you connect with someone, even if you mistyped the number... Doesn't look random to me.
However I did not read the 85 pages paper... Maybe it's addressed there.
> By constructing a search query that joins together 32 randomly generated identifiers using the OR operator, the efficiency of each search increases by a factor of 32. To further increase search efficiency, randomly generated identifiers can take advantage of case insensitivity in YouTube’s search engine. A search for either "DQW4W9WGXCQ” or “dqw4w9wgxcq” will return an extant video with the ID “dQw4w9WgXcQ”. In effect, YouTube will search for every upper- and lowercase permutation of the search query, returning all matches. Each alphabetical character in positions 1 to 10 increases search efficiency by a factor of 2. Video identifiers with only alphabetical characters in positions 1 to 10 (valid characters for position 11 do not benefit from case-insensitivity) will maximize search efficiency, increasing search efficiency by a factor of 1024. By constructing search queries with 32 randomly generated alphabetical identifiers, each search can effectively search 32,768 valid video identifiers.
They also mention some caveats to this method, namely, that it only includes publicly listed videos:
> As our method uses YouTube search, our random set only includes public videos. While an alternative brute force method, involving entering video IDs directly without the case sensitivity shortcut that requires the search engine, would include unlisted videos, too, it still would not include private videos. If our method did include unlisted videos, we would have omitted them for ethical reasons anyway to respect users’ privacy through obscurity (Selinger & Hartzog, 2018). In addition to this limitation, there are considerations inherent in our use of the case insensitivity shortcut, which trusts the YouTube search engine to provide all matching results, and which oversamples IDs with letters, rather than numbers or symbols, in their first ten characters. We do not believe these factors meaningfully affect the quality of our data, and as noted above a more direct “brute force” method - even for the purpose of generating a purely random sample to compare to our sample - would not be computationally realistic.
That's very clever. Presumably the video ID in the URL is case-sensitive, but then YouTube went out of their way to index a video's ID for text search, which made this possible.
Good observation, but they also acknowledge:
> there are considerations inherent in our use of the case insensitivity shortcut, which trusts the YouTube search engine to provide all matching results, and which oversamples IDs with letters, rather than numbers or symbols, in their first ten characters. We do not believe these factors meaningfully affect the quality of our data, and as noted above a more direct “brute force” method - even for the purpose of generating a purely random sample
In short I do believe that the sample is valuable, but it is not a true random sample in the spirit that the post is written, there is a heuristic to have "more hits"
No I think the author works at Google and have a couple of hours weekly or monthly to do "experiments", but the employment terms forces the ownership of the "experiments" to Google but since the projects may not align with Googles image or reputation they wish not to be officially associated with it. They own it but do not "stand behind it" officially
> Jason found a couple of cheats that makes the method roughly 32,000 times as efficient, meaning our “phone call” connects lots more often