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How useful was this post? Click on a star to rate it! As you found this post useful Follow us on social media! An example of this would be: The. To perform automatic categorisation, we use a simple rule based system. A list of patterns, in the form of regular expressions, was listed along with the category they corresponded to.

The full list of all rules used is given in Appendix A. The rules are listed in the author's view from the most accurate to the least accurate. To categorise a rule, each rule in order was applied to the file. Once a rule was triggered, which happened when the filename contained the pattern given by the regular expression, the file was assigned the category from the rule, and the matching procedure would stop.

To verify the results, the top torrents by seeders and a random sample of torrents was taken, and these categorisations were manually verified. Further to this, the percentage of torrents that were classified i.

This determination was primarily based on the title of the file. There were two key limitations to the procedure: firstly, we took the filename at face value, and secondly, if there was any ambiguity in the filename, we erred on the side of caution, and guess that it is legal. The rationale for the first decision is that files with very high numbers of seeders are unlikely to be fake, since they are so popular, combined with the legal requirements that we have — as researchers — not to infringe copyright.

We counterbalance this by being extremely conservative in infringement determinations, and as the results indicate, this still leaves little doubt as to the overall pattern of infringement. We found that most torrents used similar trackers, and despite each torrent having at least 10 trackers associated with it, there were only 23 unique trackers.

Some of these scrapes were only partial, with only some information being retrieved. A smaller tracker may wish to minimise their bandwidth usage by disabling this feature. For this reason, we will no longer discuss these servers in this paper. Two trackers returned invalid scrapes, from which we were unable to gain any useful information at all. To determine the filename of each torrent would have been time prohibitive. However, we hypothesised that the ranking of torrent popularity would follow a power law [18], i.

Power laws are becoming more widely acknowledged in computer science but have been well— known in biology for many years [19]. Furthermore, just 9. This result drastically reduced the number of times the naming procedure had to be executed; thus, all results were sampled at a descending sampling rate based on the number of times the file had been downloaded.

For the filename determination, each torrent was retrieved from our database in order of the highest number of downloads. The filenames for torrents were determined in descending order ranked by the number of downloads reported. Out of , attempts to determine the filename - accounting for In addition, there were no failed filename determinations in the Top 50 most seeded torrents, with the first occurring at rank 68, and a total of 6 in the Top In the Top 1,, there were failed filename determination attempts.

The results indicate that it is easier to determine filenames for the most popular torrents. Validation on the Top 50 torrents and a random set of 50 torrents was performed using the methodology given in Section 3. Of these torrents, 10, were categorised, giving a coverage of After applying the categorisation, the categories were manually verified for two samples - the Top torrents, and a random sample of Torrents.

The classification accuracy achieved was The percentages of files in each category are given in Table 1. Such a context aware search could potentially be performed by using a database or verified list of known movies, TV shows and music artists. For the uncategorised files, a sample of files was manually classified.

This is a slightly different distribution from the categorised filenames, possibly indicating that there are categories which are more easy to create rules for than others. This regularity is one reason for the low rate of unrecognised TV show torrents compared to movies and other files, such as software, where there are few or no universal conventions. Often, these torrents just have the filename and sometimes the release year.

These filenames were manually checked to determine if they were infringing or legally allowed to be distributed. Our key finding is that - of the 1, torrents in the sample — we could only confirm 3 as being non-infringing 0. We were unable to establish whether a further 16 were infringing or not 0. We did not attempt to verify the infringing status of the porn torrents, as there is a high level of ambiguity over the terms that we would generally use to determine infringements.

This is the same order of magnitude reported by popular search engine sites like Isohunt. This number is expected to increase at a lower rate with more trackers included. It would be impossible to determine an overall population value, as there are a large number of BitTorrent trackers and some are private.

But, by triangulating our estimates with those reported by torrent search engines, our results are in the right ballpark; indeed, they appear to be conservative. For each shared file, we also investigated how many times it had been shared in total. This is an important question, given the power law relationship hypothesised earlier. As part of our study, we scraped information for more than one million torrents. The Top most seeded torrents are listed in Appendix A.

This is not to say that the least popular torrents are also infringing; indeed, it is these files which are often stated to be the most widely shared [5] but the opposite appears to be true from our data. There was only one legal torrent in the Top listed in Appendix A, an open source program VLC player which uses BitTorrent as its distribution method.

Information on more than one million torrents was collected during our initial study. Just 4. We were able to assign names to more than , of the top , most downloaded torrents, accounting for This means that our headline By examining the titles in Appendix A, it is interesting to speculate about why some files are downloaded more than others, at any point in time.

However, you can also observe cases where movies were less successful in the cinema but also popular for downloading. Is there a link between accessibility and popularity? Or does the ease with which users can download infringing content make popularity a less relevant factor? Or are some torrents actually for fake files, given the high seed count and out-of-date nature of the material?

Further research is required to better understand the decision making processes that users make when they are searching for and downloading infringing content, and also to accurately detect torrents for fake files. As expected, the results varied in absolute terms e. The results from the replication study are described below. We used the same initial list of trackers from the first study, however, not all of the same trackers returned usable scrapes.

There is also some measurement error to be expected — some trackers may be still functioning, but shaping their responses when traffic is slow, and disconnecting at other times. Note that the tracker from the previous study which gave the highest results in the firs study desi6 did not provide a usable scrape in the replication study.

This resulted in overall lower seeder numbers than recorded in the original study. We also pruned one tracker openbittorrent. The results of the replication study indicate that our data are very reliant on the trackers used; some will be more popular in music circles, some more popular for TV shows and movies, and some will have a very short lifespan. Further longitudinal observation and analysis will be required to establish long-term patterns of activity.

From the new sample, 98, files were given a filename, out of 2,, torrent files found, and , filename guessing attempts. The sampling method used was random this time random torrents were chosen to be named , as opposed to using the most downloaded files in the original study.

Despite this, the overall ranking and relative proportions of material in different categories remained consistent, as shown in Table 3. This represents the minimum number of seeders per file for the new sample. The overall maximum number of seeders currently online was 6,, In terms of infringement, in the most downloaded list, there were 2 non- infringing files and 1 unknown.

The non-infringing files were Windows 7 loaders which - while they are intended to support illegal activity - are not themselves generally infringing. Again, this illustrates some of the difficulty in automatically categorizing porn files as being infringing or not. In summary, the results of the replication study support the conclusions of the original study; importantly, we struggled to find any material which was not infringing. In isohunt. The goal here was to establish intent; what were people searching for, and was it likely to be infringing content?

Appendix C contains a list of the Top search terms, and the manual categorizations assigned to each case3. We couldn't identify any content which was not infringing or illegal using this technique. The results indicate that while there are some changes to the relative percentages of material being searched for in each category, the overall ranks of each category generally remained consistent.

We have also presented the results of an initial and follow-up study — with broader and narrower sampling respectively —indicating that the overwhelming majority of the most popular content on BitTorrent is infringing. As hypothesized, we found that there was a power law relationship between the number of downloads and popularity, but that the result was worse than expected, since just 4.

In addition, for the 1, most popular, we were only able to identify three files which were not infringing content. Our replication study — which excluded trackers reporting high download rates — the relative rankings between the different categories of content remained largely the same. Furthermore, we validated the study by comparing what users are searching for to establish their intent and what they are actually downloading, and once again, we found the same pattern of use, i.

There are a number of limitations in this study, and it is important to recognise them when interpreting the results. Firstly, any study which relies on sampling has the potential for a number of different types of bias to influence the results [22].

We sampled from a list of the most popular public trackers for the most popular searches. This did not include private trackers, and given our hypothesis of a power law, did not provide coverage of the least popular public trackers and the least popular torrents. It is likely, though, that the files being shared on those trackers would be the type of content outlined in [5].

This reflects the fact that BitTorrent is a great technology that can be used to distribute material efficiently and effectively whether it is popular or not. Possibly the greatest limitation for the study is that we did not ourselves download any infringing content, as this would be illegal both in a civil and criminal sense, since some of the material was child porn. Instead, we have relied on the observation that if a file is labelled and advertised with a certain title, and many independent users have downloaded that title and are also seeding it, then it is highly likely that the file is what it claims to be.

This follows from the law of large numbers [23]; indeed, we would be more concerned about interpreting the data if fewer users were involved. However, we are currently developing other methods to validate the results by performing text mining of the reviews provided by independent users on various torrent sites, and verifying the reviewers by grouping them by reputation.

From a technical perspective, the most pressing limitation to this research is that large BitTorrent sites - such as The Pirate Bay — are moving away from the public tracker based model. These technologies can reduce and possibly eliminate the need for trackers, thus removing the main source of data used in this research.

The development and implementation of these technologies appears to be a response to the various lawsuits that have been brought about against the operators of BitTorrent search sites and trackers. Apart from addressing these limitations, we are focused on improving the automatic labelling of files, both by category and by their legality.

The video sharing website YouTube, for example, uses a content management system called ContentID [24] to determine if a new video uploaded is a recognised infringing copy of a copyrighted file. A system like this could be extended toward the automatic categorisation of downloaded files, but suffers from the problem that the files must be downloaded in order to verify them. Apart from the legal issues involved, this would require a significant amount of bandwidth to download the millions of files being shared.

Possibly, a sampling methodology could be used to download only a small portion of the file to determine if it is infringing, if the files contained embedded traitor tracing codes that were robust against attacks to remove them [25]. This project received financial support from Village Roadshow.

BitTorrent for Dummies. New York: Wiley. Trust based access control framework for P2P file-sharing systems. Detecting and tracing copyright infringements in P2P networks. Analyzing and Improving BitTorrent Performance. The BitTorrent P2P file-sharing system: measurements and analysis. Reducing digital copyright infringement without restricting innovation. Boalt Working Papers in Public Law.

Is someone tracking P2P users? The index poisoning attack in P2P file-sharing systems.

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