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Alongside films and TV, you will find torrents for applications, games, novels, and audio. The interface is somewhat clunky, but overall it is an exceptional option for Your Pirate Bay. On top of that, we did not experience any pop-ups if we saw the website! Yolamovies HD Movies Quality. Torrentz2 is a very simple search tool that indexes only over 61,, torrents. It is not as feature-packed as torrent platforms such as Zooqle, but it certainly gets the job done. Torrentz2 is more comprehensive than any single torrent website since it searches multiple databases at the same time.
If youre searching for a simple, no-frills solution into The Pirate Bay, Torrentz2 offers you covered. IDope is a rather new addition into the torrent scene, but its become incredibly common and for good reason. The tracker sample is then scraped Section 3. Filenames are determined from the scrapes Section 3. Categorisation of the torrents is performed Section 3.
The number of infringing files is determined Section 3. All trackers listed for each of these files was then selected for sampling. This scrape was downloaded in a similar way to a normal HTTP download. If the download was interrupted, the scrape was not attempted again in that iteration.
An interrupted download could still be useful, however, as it would contain valid scrape information up to the end of the downloaded portion. When parsing the scrape data, the consistency of the file was not verified to ensure that information could be gathered from interrupted downloads. Rather, any valid data for each file was collected and saved into a database. However, in many cases, the trackers we retrieved data from indicated that all files had been downloaded 10 times, even when the number of current seeders was in the thousands.
This was clearly impossible, as — by definition - a seeder is someone who has completely downloaded a file. Thus, the downloaded number was excluded from our results. In this paper, therefore, the term 'downloads' refers to the number of seeders a file has.
However, by querying external data sources, it is possible to correlate the info hashes with file titles. One of the advantages we have here is that — like searching for internet pornography — users need to search for terms of interest, and search engines thus provide a convenient means to perform reverse lookups .
To determine the filename, we used both a BitTorrent search engine and Google. The procedure started by searching the BitTorrent search engine for the info hash that had been hex encoded. If the BitTorrent search engine had the torrent that generated this info hash, it would return the torrent, including the names of the files contained in it.
We then parsed the search results to extract only the filename, and stored the resulting filename in the database. If this procedure failed, we performed a Google search for the hex encoded info hash. If results were returned from Google, we ranked them in order of appearance.
If the title of the search result i. If the hex hash was not in the title, we used the title as our filename result. A full parsing of the returned results remains a significant problem for automatic parsing, and was considered out of scope for this methodology. To determine the accuracy of the filename determination procedure, the results were verified by performing a reverse lookup. To do this, we selected the top 50 seeded torrents with filenames, and a random sample of 50 torrents from the full set of named torrents, as our test set.
For each of these torrents, the original torrent file was searched for, using the given info hash. The torrent file was then downloaded, and the info hash re-calculated to verify that the torrent was correct. This sampling method was chosen to ensure that there were no biases between the top torrents, compared to a representative sample of the full set of named torrents.
Category determination was easier for some files than others. This format changes a little bit as well between release groups and sometimes is a different format altogether. 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 , i. Power laws are becoming more widely acknowledged in computer science but have been well— known in biology for many years . 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  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.
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Chris Muto. Wendy Wells-Gunkel. Kimberly Langley. Kiya Roberts. Eric Banzon. Rob Goodman. Christine Hitt. Marc Linsalata. Raymond T. Lili Asvar. John Bartlebaugh. Eng: To solve a mysterious and macabre murder, Homicide brings in Brother Sam, a minister with a criminal past. But when Dexter meets Brother Sam, he doesn't recognize a man of God, but a fellow killer who might have found a cover even better than his own. Debra, now a local hero, is surprised by two life changing proposals.
Eng: Dexter is confronted with a sobering glimpse of his own potential future when a serial killer from his past makes a startling reappearance. Debra is uncomfortable in her new job. And Travis struggles to keep his mentor happy, as he and Gellar prepare their next twisted tableau, ensuring Debra's next task will be a daunting one.
Eng: Much to his own surprise, an emergency with Harrison has Dexter leaning on Brother Sam for support as he questions the idea of faith. But when Dexter is confronted with Gellar and Travis' next awful tableau, he is forced to ask if faith, in the wrong hands, is deadly. With proof of a religiously motivated killer, Homicide hunts for a zealot, with Debra giving her first official Press Conference.
As Homicide narrows in on a suspect, a winged messenger has an unexpected revelation for Dexter. Eng: With the help of his newfound friend, Brother Sam, Dexter wonders if there is light within him to counter the darkness. The search for the Doomsday Killers takes Dexter in a new direction, while Batista and Quinn pay a visit to the university where Professor Gellar taught. Due to departmental regulation following the shooting, Debra is forced to begin therapy.
Eng: Dexter is caught up in a very personal case that awakens the needs of his Dark Passenger. Meanwhile, Debra feels overwhelmed by her new Lieutenant duties, made all the more complicated when she finds out Quinn slept with a witness in the Doomsday case. Eng: Dexter finds himself teamed up with a fellow Dark Passenger when he takes a road trip to Nebraska to tie up some loose ends from the past. As Debra continues to lead the investigation into the Doomsday killings, she deals with the complications of being a lieutenant.
Eng: Dexter uses lessons he learned from Brother Sam to follow up on some new leads in the Doomsday investigation. Debra butts heads with Captain LaGuerta over the case of a dead call girl. Eng: Dexter finds help from an unexpected avenue while on the hunt for the Doomsday Killers, all the while trying to stay one step ahead of the Homicide Division.
Debra continues to see her therapist and discovers something new about herself. Eng: Dexter tries to figure out the Doomsday Killers' next victim before it's too late.
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