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02.26.2006-03.02.2006, bhance
(04.30.2007 - somebody applied this same idea, to, like, the 9 billionth degree - kudos, Felix Heinen!) The BackgroundA few days ago I ran across this article, 'The MySpace Mirage' (link) via digg.com.The upshot of the whole thing: MySpace's user numbers are inflated, because a vast percentage of its users log in once and never come back. Remember 'Friendster' and how that was the new hotness for about a year? Yeah, well, same thing ... Even though slamming MySpace is an amateur sport these days ... Everything about this company is inflated, and the fact that anybody with brain one invested in a site based on ColdFusion is pretty much a sign that we're all doomed. That notwithstanding: Some minor points
a) Every MySpace user page has a "Last Login" listed on it.
The IdeaWrite a script that hits a MySpace pages, notes the 'Last Login', notes their 'Friends', and then recursively does the same thing for those friends. All the while shuffling that info into a database so I can run some calculations on it later.Nonscientific, and statistically about as strong as jello, but what the heck. Let's hop to it. Some PseudoCode
while (true) {
get an un-checked username
if we're sure we haven't checked this person before {
if their awful myspace page actually loads {
put their last login (and today's date, so we can compare) into a database
put their first 5 friends in the unchecked list
}
}
}
There's also some code in there for dealing with oddly-formatted pages and database checks,
but you get the idea.
The SetupThere's no way I'm going to subject any machine I actually like to that many MySpace pages, so I'll run this on one of the old boxes I have lying around: an AMD K6/450 running Redhat Fedora Core 4, PHP 4.4.2, and MySql 4.1.18.Add one cable modem and throttle the thing to one request every 2 seconds to be a nice guy. 24h x 60min x 60 seconds / 2 = 43,200 users a day, minus network burps, minus the occasional erroneous page, so I guess it'll come in about 30k/day. Updated - the script will occasionally die for an unknown reason. Probably too much emo for one poor server to deal with. So I'll periodically restart the thing. I'll call 20 or 30k a nice wide sampling, so I'll come back in a few days. 2 days, and 31,264 rows laterDone. Got 31,264 results to play with.Some pseudocode on the stats analysis:
for all 30k rows {
note the date checked
note the last login date
subtract one from the other to get N days difference
if (results[Ndays]) {
results[Ndays]++
} else {
results[Ndays] = 1;
}
}
sort results
iterate over results
while calculating percentages
The upshot? Something like 82% of the MS users 'last login' clock in under 5 days.
Simultaneously amazing and depressing. Are there really that many struggling,
horrible bands out there?
Results- 0 days: 12576 (40.23 %) - 1 days: 8912 (28.51 %) - 2 days: 1950 (6.24 %) - 3 days: 1127 (3.60 %) - 4 days: 686 (2.19 %) - 5 days: 468 (1.50 %) - 6 days: 357 (1.14 %) - 7 days: 251 (0.80 %) - 8 days: 188 (0.60 %) - 9 days: 152 (0.49 %) - 10 days: 135 (0.43 %) - 11 days: 121 (0.39 %) - 12 days: 120 (0.38 %) - 13 days: 106 (0.34 %) - 14 days: 99 (0.32 %) - 15 days: 82 (0.26 %) - 16 days: 63 (0.20 %) - 17 days: 63 (0.20 %) - 18 days: 63 (0.20 %) - 19 days: 46 (0.15 %) - 20 days: 50 (0.16 %) - 21 days: 53 (0.17 %) - 22 days: 50 (0.16 %) - 23 days: 44 (0.14 %) - 24 days: 36 (0.12 %) - 25 days: 42 (0.13 %) - 26 days: 45 (0.14 %) - 27 days: 45 (0.14 %) - 28 days: 35 (0.11 %) - 29 days: 26 (0.08 %) - 30 days: 19 (0.06 %) - 31 days: 29 (0.09 %) - 32 days: 32 (0.10 %) - 33 days: 23 (0.07 %) - 34 days: 25 (0.08 %) - 35 days: 22 (0.07 %) - 36 days: 40 (0.13 %) - 37 days: 25 (0.08 %) - 38 days: 22 (0.07 %) - 39 days: 27 (0.09 %) - 40 days: 22 (0.07 %) - 41 days: 20 (0.06 %) - 42 days: 14 (0.04 %) - 43 days: 19 (0.06 %) - 44 days: 19 (0.06 %) - 45 days: 25 (0.08 %) - 46 days: 16 (0.05 %) - 47 days: 19 (0.06 %) - 48 days: 28 (0.09 %) - 49 days: 24 (0.08 %) - 50 days: 13 (0.04 %) - 51 days: 18 (0.06 %) - 52 days: 16 (0.05 %) - 53 days: 14 (0.04 %) - 54 days: 11 (0.04 %) - 55 days: 12 (0.04 %) - 56 days: 15 (0.05 %) - 57 days: 10 (0.03 %) - 58 days: 17 (0.05 %) - 59 days: 9 (0.03 %) - 60 days: 14 (0.04 %) - 61 days: 15 (0.05 %) - 62 days: 11 (0.04 %) - 63 days: 22 (0.07 %) - 64 days: 3 (0.01 %) - 65 days: 10 (0.03 %) - 66 days: 8 (0.03 %) - 67 days: 9 (0.03 %) - 68 days: 23 (0.07 %) - 69 days: 15 (0.05 %) - 70 days: 27 (0.09 %) - 71 days: 7 (0.02 %) - 72 days: 12 (0.04 %) - 73 days: 10 (0.03 %) - 74 days: 11 (0.04 %) - 75 days: 14 (0.04 %) - 76 days: 17 (0.05 %) - 77 days: 7 (0.02 %) - 78 days: 11 (0.04 %) - 79 days: 20 (0.06 %) - 80 days: 11 (0.04 %) - 81 days: 6 (0.02 %) - 82 days: 16 (0.05 %) - 83 days: 5 (0.02 %) - 84 days: 9 (0.03 %) - 85 days: 8 (0.03 %) - 86 days: 14 (0.04 %) - 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