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Thanks Megan! That is helpful. Also, did you see my tweet about the ID structure of Youtube ids? I'm not entirely convinced that their ids are truly random. If there was any structure to the ids, that might give clues as to the level of activity for a time range.
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That's interesting -- my assumption was that they're random. I think that's the assumption the aforementioned paper relies on as well... I think it's entirely possible that the IDs are randomly selected since the ID space is so large, but I have no clue if that's true...
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For me it makes sense to aggregate data on a channel level, a large-scale crawl could use "subscribed lists" and get the largest component of the node. A good place to start is social blade data. I crawled some (72M videos)
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