TorrentWatch

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HOW WE COLLECTED THE DATA
Looking into P2P file-sharing clients we found videos, mp3’s, software, and even EBooks which are all very commonly traded. We found none so popular as movie files. Choosing movies to be our test subject, we next decided on choosing the site Mininova.org as our torrent site because of its vast popularity, ubiquity and generic feel. We then picked the 3 most seeded movies from there as our test files (Baby Mama, How to rob a Bank, Diving Bell and the Butterfly -- each produced in 2008) and for each hour in a 24 hour period span, we took turns recording the amount of seeders who were downloading each of the three movies without sharing it first (seeding at less than 100%), and those who were downloading it while sharing it too (seeding at 100%). [We assumed that one arbitrary 24 hour period of time would be a fair representation of the world’s P2P activities because there are so many variables to find reason to distinguish the week from the weekend and therefore one day from another]. From these data measurements we traced each of the IP addresses, which ranged in numbers from twenty to near ten thousand P2P users throughout the world per movie using Visual IP trace 2008. The program converted the IP names to their corresponding countries of the world and then outputted them to Excel spreadsheets.



We then manually tabulated the results into the six continents of the world: North America, South America, Europe, Asia, Africa, and Australia.To standardize the vast variation in the quantity of users per continent and to minimize variables; we divided the seeders by the amount of leechers to define the ratio of seeders to leechers.
So with our given data, we have the seeder/leecher ratio of 3 popular movies in all six continents of the world, and our task was to figure out if the time of day had any bearing on the global ratio. One issue we ran into was standardizing the time zones so we could view them universally because our data was collected using PST (GMT-8) time. To standardize the time zones, we picked the central time zone on the country of highest population in the continent to be the “main” time zone. So in North America the highest population country is the United States and the central time zone is GMT-6; In South America, Brazil (GMT-4); in Europe, Russia (GMT+3); in Asia, China (GMT+5); in Australia, Sydney (GMT+7) and no info on Antarctica reported. So for example, an 8 AM (GMT-8) time collection would be the same as 10AM for North America in relative terms to GMT-8, 12PM for South America in relative terms to GMT-8, and likewise 9PM for Asia, 7PM for Russia and 5PM for Africa. We adjusted our data accordingly.